Languages#
The following fold out menus contain all of the languages included in Asent. The indicate that the language lexicon is autogenerated and therefore experimental.
English
License: MIT
The lexicons contain a list of rated words for English along with intensifiers, contrastive conjugations and negations. The words are rated by Hutto and Gilbert, (2014).
The lexicons contain a total of 7504 rated words, 59 negations, 84 intensifiers, and 1 contrastive conjugations.
You can load the English lexicons using:
from asent import lexicons
rated_words = lexicons.get("lexicon_en_v1")
negations = lexicons.get("negations_en_v1")
intensifiers = lexicons.get("intensifiers_en_v1")
cconj = lexicons.get("contrastive_conj_en_v1")
License: GNU 3.0
This sentiment lexicon were generated by Chen, Y., & Skiena, S. (2014) using by propagation using a knowledge graph - a graphical representation of real-world entities and the links between them. The general intuition is that words which are closely linked on a knowledge graph probably have similar sentiment polarities. For this project, sentiments were generated based on English sentiment lexicons.
from asent import lexicons
rated_words = lexicons.get("lexicon_en_chen_skiena_2014_v1.txt")
Danish
License: Apache 2.0
The Danish lexical resources contain a list of rated words derived from Afinn. The lexicon also include intensifier, contrastive conjugations and negations. It includes a total of 6593 rated lemmas along with intensifier, contrastive conjugations and negations.
You can load the Danish lexicons using:
from asent import lexicons
rated_words = lexicons.get("lexicon_da_afinn_v1")
negations = lexicons.get("negations_da_v1")
intensifiers = lexicons.get("intensifiers_da_v1")
cconj = lexicons.get("contrastive_conj_da_v1")
License: CC BY-NC 3.0
Beyond the default lexicon Asent also include a series of words rated in context by Dalsgaard, Lauridsen and Svendsen (2019). Which have been lemmatized using DaCy (Enevoldsen et al., 2021). This dictionary:
rated_words = lexicons.get("lexicon_da_sentida_lemma_v1")
License: GNU 3.0
This sentiment lexicon were generated by Chen, Y., & Skiena, S. (2014) using by propagation using a knowledge graph - a graphical representation of real-world entities and the links between them. The general intuition is that words which are closely linked on a knowledge graph probably have similar sentiment polarities. For this project, sentiments were generated based on English sentiment lexicons.
from asent import lexicons
rated_words = lexicons.get("lexicon_da_chen_skiena_2014_v1.txt")
Norwegian Bokmål
License: Apache 2.0
The lexicons contain a list of rated words for Norwegian. The words are rated in context by Finn A. Nielsen as a part of his package afinn. The contrastive conjugations, negations and intensifiers have been added by Center for Humanities Computing Aarhus.
The lexicon includes a total of 3214 rated words along with intensifier, contrastive conjugations and negations.
You can load the Norwegian lexicons using:
from asent import lexicons
rated_words = lexicons.get("lexicon_no_v1")
negations = lexicons.get("negations_no_v1")
intensifiers = lexicons.get("intensifiers_no_v1")
cconj = lexicons.get("contrastive_conj_no_v1")
License: GNU 3.0
This sentiment lexicon were generated by Chen, Y., & Skiena, S. (2014) using by propagation using a knowledge graph - a graphical representation of real-world entities and the links between them. The general intuition is that words which are closely linked on a knowledge graph probably have similar sentiment polarities. For this project, sentiments were generated based on English sentiment lexicons.
from asent import lexicons
rated_words = lexicons.get("lexicon_no_chen_skiena_2014_v1.txt")
Swedish
License: MIT
The default lexicons is the same as the one used by the Swedish Vader, (vaderSentiment-swedish) available on PyPI.
The lexicon includes a total of 5501 rated orthographic words as well as with intensifier, contrastive conjugations and negations.
You can load the Swedish lexicons using:
from asent import lexicons
rated_words = lexicons.get("lexicon_se_v1")
negations = lexicons.get("negations_se_v1")
intensifiers = lexicons.get("intensifiers_se_v1")
License: GNU 3.0
This sentiment lexicon were generated by Chen, Y., & Skiena, S. (2014) using by propagation using a knowledge graph - a graphical representation of real-world entities and the links between them. The general intuition is that words which are closely linked on a knowledge graph probably have similar sentiment polarities. For this project, sentiments were generated based on English sentiment lexicons.
from asent import lexicons
rated_words = lexicons.get("lexicon_se_chen_skiena_2014_v1.txt")
Emoji
License: MIT
The rating of emojis is created by applying Vader (Hutto and Gilbert, 2014) to the description of the emojis. Afterwards, the scores are normalized to be between -5 and 5.
It contains a total of 3570 rated emojis of which 391 of them has a non-zero rating.
You can load the Emoji lexicon using:
from asent import lexicons
rated_words = lexicons.get("emoji_v1")
Experimental
These sentiment lexicons were generated by Chen, Y., & Skiena, S. (2014) using by propagation using a knowledge graph - a graphical representation of real-world entities and the links between them. The general intuition is that words which are closely linked on a knowledge graph probably have similar sentiment polarities. For this project, sentiments were generated based on English sentiment lexicons.
Afrikaans
License: GNU 3.0
This sentiment lexicon were generated by Chen, Y., & Skiena, S. (2014) using by propagation using a knowledge graph - a graphical representation of real-world entities and the links between them. The general intuition is that words which are closely linked on a knowledge graph probably have similar sentiment polarities. For this project, sentiments were generated based on English sentiment lexicons.
from asent import lexicons
rated_words = lexicons.get("lexicon_af_chen_skiena_2014_v1.txt")
Albanian
License: GNU 3.0
This sentiment lexicon were generated by Chen, Y., & Skiena, S. (2014) using by propagation using a knowledge graph - a graphical representation of real-world entities and the links between them. The general intuition is that words which are closely linked on a knowledge graph probably have similar sentiment polarities. For this project, sentiments were generated based on English sentiment lexicons.
from asent import lexicons
rated_words = lexicons.get("lexicon_sq_chen_skiena_2014_v1.txt")
Arabic
License: GNU 3.0
This sentiment lexicon were generated by Chen, Y., & Skiena, S. (2014) using by propagation using a knowledge graph - a graphical representation of real-world entities and the links between them. The general intuition is that words which are closely linked on a knowledge graph probably have similar sentiment polarities. For this project, sentiments were generated based on English sentiment lexicons.
from asent import lexicons
rated_words = lexicons.get("lexicon_ar_chen_skiena_2014_v1.txt")
Arabic
License: GNU 3.0
This sentiment lexicon were generated by Chen, Y., & Skiena, S. (2014) using by propagation using a knowledge graph - a graphical representation of real-world entities and the links between them. The general intuition is that words which are closely linked on a knowledge graph probably have similar sentiment polarities. For this project, sentiments were generated based on English sentiment lexicons.
from asent import lexicons
rated_words = lexicons.get("lexicon_ar_chen_skiena_2014_v1.txt")
Aragonese
License: GNU 3.0
This sentiment lexicon were generated by Chen, Y., & Skiena, S. (2014) using by propagation using a knowledge graph - a graphical representation of real-world entities and the links between them. The general intuition is that words which are closely linked on a knowledge graph probably have similar sentiment polarities. For this project, sentiments were generated based on English sentiment lexicons.
from asent import lexicons
rated_words = lexicons.get("lexicon_an_chen_skiena_2014_v1.txt")
Aragonese
License: GNU 3.0
This sentiment lexicon were generated by Chen, Y., & Skiena, S. (2014) using by propagation using a knowledge graph - a graphical representation of real-world entities and the links between them. The general intuition is that words which are closely linked on a knowledge graph probably have similar sentiment polarities. For this project, sentiments were generated based on English sentiment lexicons.
from asent import lexicons
rated_words = lexicons.get("lexicon_an_chen_skiena_2014_v1.txt")
Armenian
License: GNU 3.0
This sentiment lexicon were generated by Chen, Y., & Skiena, S. (2014) using by propagation using a knowledge graph - a graphical representation of real-world entities and the links between them. The general intuition is that words which are closely linked on a knowledge graph probably have similar sentiment polarities. For this project, sentiments were generated based on English sentiment lexicons.
from asent import lexicons
rated_words = lexicons.get("lexicon_hy_chen_skiena_2014_v1.txt")
Azerbaijani
License: GNU 3.0
This sentiment lexicon were generated by Chen, Y., & Skiena, S. (2014) using by propagation using a knowledge graph - a graphical representation of real-world entities and the links between them. The general intuition is that words which are closely linked on a knowledge graph probably have similar sentiment polarities. For this project, sentiments were generated based on English sentiment lexicons.
from asent import lexicons
rated_words = lexicons.get("lexicon_az_chen_skiena_2014_v1.txt")
Basque
License: GNU 3.0
This sentiment lexicon were generated by Chen, Y., & Skiena, S. (2014) using by propagation using a knowledge graph - a graphical representation of real-world entities and the links between them. The general intuition is that words which are closely linked on a knowledge graph probably have similar sentiment polarities. For this project, sentiments were generated based on English sentiment lexicons.
from asent import lexicons
rated_words = lexicons.get("lexicon_eu_chen_skiena_2014_v1.txt")
Belarusian
License: GNU 3.0
This sentiment lexicon were generated by Chen, Y., & Skiena, S. (2014) using by propagation using a knowledge graph - a graphical representation of real-world entities and the links between them. The general intuition is that words which are closely linked on a knowledge graph probably have similar sentiment polarities. For this project, sentiments were generated based on English sentiment lexicons.
from asent import lexicons
rated_words = lexicons.get("lexicon_be_chen_skiena_2014_v1.txt")
Bulgarian
License: GNU 3.0
This sentiment lexicon were generated by Chen, Y., & Skiena, S. (2014) using by propagation using a knowledge graph - a graphical representation of real-world entities and the links between them. The general intuition is that words which are closely linked on a knowledge graph probably have similar sentiment polarities. For this project, sentiments were generated based on English sentiment lexicons.
from asent import lexicons
rated_words = lexicons.get("lexicon_bg_chen_skiena_2014_v1.txt")
Bengali
License: GNU 3.0
This sentiment lexicon were generated by Chen, Y., & Skiena, S. (2014) using by propagation using a knowledge graph - a graphical representation of real-world entities and the links between them. The general intuition is that words which are closely linked on a knowledge graph probably have similar sentiment polarities. For this project, sentiments were generated based on English sentiment lexicons.
from asent import lexicons
rated_words = lexicons.get("lexicon_bn_chen_skiena_2014_v1.txt")
Breton
License: GNU 3.0
This sentiment lexicon were generated by Chen, Y., & Skiena, S. (2014) using by propagation using a knowledge graph - a graphical representation of real-world entities and the links between them. The general intuition is that words which are closely linked on a knowledge graph probably have similar sentiment polarities. For this project, sentiments were generated based on English sentiment lexicons.
from asent import lexicons
rated_words = lexicons.get("lexicon_br_chen_skiena_2014_v1.txt")
Bosnian
License: GNU 3.0
This sentiment lexicon were generated by Chen, Y., & Skiena, S. (2014) using by propagation using a knowledge graph - a graphical representation of real-world entities and the links between them. The general intuition is that words which are closely linked on a knowledge graph probably have similar sentiment polarities. For this project, sentiments were generated based on English sentiment lexicons.
from asent import lexicons
rated_words = lexicons.get("lexicon_bs_chen_skiena_2014_v1.txt")
Catalan
License: GNU 3.0
This sentiment lexicon were generated by Chen, Y., & Skiena, S. (2014) using by propagation using a knowledge graph - a graphical representation of real-world entities and the links between them. The general intuition is that words which are closely linked on a knowledge graph probably have similar sentiment polarities. For this project, sentiments were generated based on English sentiment lexicons.
from asent import lexicons
rated_words = lexicons.get("lexicon_ca_chen_skiena_2014_v1.txt")
Czech
License: GNU 3.0
This sentiment lexicon were generated by Chen, Y., & Skiena, S. (2014) using by propagation using a knowledge graph - a graphical representation of real-world entities and the links between them. The general intuition is that words which are closely linked on a knowledge graph probably have similar sentiment polarities. For this project, sentiments were generated based on English sentiment lexicons.
from asent import lexicons
rated_words = lexicons.get("lexicon_cs_chen_skiena_2014_v1.txt")
Chinese
License: GNU 3.0
This sentiment lexicon were generated by Chen, Y., & Skiena, S. (2014) using by propagation using a knowledge graph - a graphical representation of real-world entities and the links between them. The general intuition is that words which are closely linked on a knowledge graph probably have similar sentiment polarities. For this project, sentiments were generated based on English sentiment lexicons.
from asent import lexicons
rated_words = lexicons.get("lexicon_zh_chen_skiena_2014_v1.txt")
Croatian
License: GNU 3.0
This sentiment lexicon were generated by Chen, Y., & Skiena, S. (2014) using by propagation using a knowledge graph - a graphical representation of real-world entities and the links between them. The general intuition is that words which are closely linked on a knowledge graph probably have similar sentiment polarities. For this project, sentiments were generated based on English sentiment lexicons.
from asent import lexicons
rated_words = lexicons.get("lexicon_cr_chen_skiena_2014_v1.txt")
Czech
License: GNU 3.0
This sentiment lexicon were generated by Chen, Y., & Skiena, S. (2014) using by propagation using a knowledge graph - a graphical representation of real-world entities and the links between them. The general intuition is that words which are closely linked on a knowledge graph probably have similar sentiment polarities. For this project, sentiments were generated based on English sentiment lexicons.
from asent import lexicons
rated_words = lexicons.get("lexicon_cs_chen_skiena_2014_v1.txt")
Dutch
License: GNU 3.0
This sentiment lexicon were generated by Chen, Y., & Skiena, S. (2014) using by propagation using a knowledge graph - a graphical representation of real-world entities and the links between them. The general intuition is that words which are closely linked on a knowledge graph probably have similar sentiment polarities. For this project, sentiments were generated based on English sentiment lexicons.
from asent import lexicons
rated_words = lexicons.get("lexicon_nl_chen_skiena_2014_v1.txt")
Estonian
License: GNU 3.0
This sentiment lexicon were generated by Chen, Y., & Skiena, S. (2014) using by propagation using a knowledge graph - a graphical representation of real-world entities and the links between them. The general intuition is that words which are closely linked on a knowledge graph probably have similar sentiment polarities. For this project, sentiments were generated based on English sentiment lexicons.
from asent import lexicons
rated_words = lexicons.get("lexicon_et_chen_skiena_2014_v1.txt")
Esperanto
License: GNU 3.0
This sentiment lexicon were generated by Chen, Y., & Skiena, S. (2014) using by propagation using a knowledge graph - a graphical representation of real-world entities and the links between them. The general intuition is that words which are closely linked on a knowledge graph probably have similar sentiment polarities. For this project, sentiments were generated based on English sentiment lexicons.
from asent import lexicons
rated_words = lexicons.get("lexicon_eo_chen_skiena_2014_v1.txt")
Faroese
License: GNU 3.0
This sentiment lexicon were generated by Chen, Y., & Skiena, S. (2014) using by propagation using a knowledge graph - a graphical representation of real-world entities and the links between them. The general intuition is that words which are closely linked on a knowledge graph probably have similar sentiment polarities. For this project, sentiments were generated based on English sentiment lexicons.
from asent import lexicons
rated_words = lexicons.get("lexicon_fo_chen_skiena_2014_v1.txt")
Finnish
License: GNU 3.0
This sentiment lexicon were generated by Chen, Y., & Skiena, S. (2014) using by propagation using a knowledge graph - a graphical representation of real-world entities and the links between them. The general intuition is that words which are closely linked on a knowledge graph probably have similar sentiment polarities. For this project, sentiments were generated based on English sentiment lexicons.
from asent import lexicons
rated_words = lexicons.get("lexicon_fi_chen_skiena_2014_v1.txt")
French
License: GNU 3.0
This sentiment lexicon were generated by Chen, Y., & Skiena, S. (2014) using by propagation using a knowledge graph - a graphical representation of real-world entities and the links between them. The general intuition is that words which are closely linked on a knowledge graph probably have similar sentiment polarities. For this project, sentiments were generated based on English sentiment lexicons.
from asent import lexicons
rated_words = lexicons.get("lexicon_fr_chen_skiena_2014_v1.txt")
Galician
License: GNU 3.0
This sentiment lexicon were generated by Chen, Y., & Skiena, S. (2014) using by propagation using a knowledge graph - a graphical representation of real-world entities and the links between them. The general intuition is that words which are closely linked on a knowledge graph probably have similar sentiment polarities. For this project, sentiments were generated based on English sentiment lexicons.
from asent import lexicons
rated_words = lexicons.get("lexicon_gl_chen_skiena_2014_v1.txt")
Georgian
License: GNU 3.0
This sentiment lexicon were generated by Chen, Y., & Skiena, S. (2014) using by propagation using a knowledge graph - a graphical representation of real-world entities and the links between them. The general intuition is that words which are closely linked on a knowledge graph probably have similar sentiment polarities. For this project, sentiments were generated based on English sentiment lexicons.
from asent import lexicons
rated_words = lexicons.get("lexicon_ka_chen_skiena_2014_v1.txt")
German
License: GNU 3.0
This sentiment lexicon were generated by Chen, Y., & Skiena, S. (2014) using by propagation using a knowledge graph - a graphical representation of real-world entities and the links between them. The general intuition is that words which are closely linked on a knowledge graph probably have similar sentiment polarities. For this project, sentiments were generated based on English sentiment lexicons.
from asent import lexicons
rated_words = lexicons.get("lexicon_de_chen_skiena_2014_v1.txt")
German
License: GNU 3.0
This sentiment lexicon were generated by Chen, Y., & Skiena, S. (2014) using by propagation using a knowledge graph - a graphical representation of real-world entities and the links between them. The general intuition is that words which are closely linked on a knowledge graph probably have similar sentiment polarities. For this project, sentiments were generated based on English sentiment lexicons.
from asent import lexicons
rated_words = lexicons.get("lexicon_de_chen_skiena_2014_v1.txt")
Gujarati
License: GNU 3.0
This sentiment lexicon were generated by Chen, Y., & Skiena, S. (2014) using by propagation using a knowledge graph - a graphical representation of real-world entities and the links between them. The general intuition is that words which are closely linked on a knowledge graph probably have similar sentiment polarities. For this project, sentiments were generated based on English sentiment lexicons.
from asent import lexicons
rated_words = lexicons.get("lexicon_gu_chen_skiena_2014_v1.txt")
Greek, modern
License: GNU 3.0
This sentiment lexicon were generated by Chen, Y., & Skiena, S. (2014) using by propagation using a knowledge graph - a graphical representation of real-world entities and the links between them. The general intuition is that words which are closely linked on a knowledge graph probably have similar sentiment polarities. For this project, sentiments were generated based on English sentiment lexicons.
from asent import lexicons
rated_words = lexicons.get("lexicon_el_chen_skiena_2014_v1.txt")
Haitian
License: GNU 3.0
This sentiment lexicon were generated by Chen, Y., & Skiena, S. (2014) using by propagation using a knowledge graph - a graphical representation of real-world entities and the links between them. The general intuition is that words which are closely linked on a knowledge graph probably have similar sentiment polarities. For this project, sentiments were generated based on English sentiment lexicons.
from asent import lexicons
rated_words = lexicons.get("lexicon_ht_chen_skiena_2014_v1.txt")
Hungarian
License: GNU 3.0
This sentiment lexicon were generated by Chen, Y., & Skiena, S. (2014) using by propagation using a knowledge graph - a graphical representation of real-world entities and the links between them. The general intuition is that words which are closely linked on a knowledge graph probably have similar sentiment polarities. For this project, sentiments were generated based on English sentiment lexicons.
from asent import lexicons
rated_words = lexicons.get("lexicon_hu_chen_skiena_2014_v1.txt")
Hebrew, modern
License: GNU 3.0
This sentiment lexicon were generated by Chen, Y., & Skiena, S. (2014) using by propagation using a knowledge graph - a graphical representation of real-world entities and the links between them. The general intuition is that words which are closely linked on a knowledge graph probably have similar sentiment polarities. For this project, sentiments were generated based on English sentiment lexicons.
from asent import lexicons
rated_words = lexicons.get("lexicon_gu_chen_skiena_2014_v1.txt")
Hindi
License: GNU 3.0
This sentiment lexicon were generated by Chen, Y., & Skiena, S. (2014) using by propagation using a knowledge graph - a graphical representation of real-world entities and the links between them. The general intuition is that words which are closely linked on a knowledge graph probably have similar sentiment polarities. For this project, sentiments were generated based on English sentiment lexicons.
from asent import lexicons
rated_words = lexicons.get("lexicon_hi_chen_skiena_2014_v1.txt")
Icelandic
License: GNU 3.0
This sentiment lexicon were generated by Chen, Y., & Skiena, S. (2014) using by propagation using a knowledge graph - a graphical representation of real-world entities and the links between them. The general intuition is that words which are closely linked on a knowledge graph probably have similar sentiment polarities. For this project, sentiments were generated based on English sentiment lexicons.
from asent import lexicons
rated_words = lexicons.get("lexicon_is_chen_skiena_2014_v1.txt")
Italian
License: GNU 3.0
This sentiment lexicon were generated by Chen, Y., & Skiena, S. (2014) using by propagation using a knowledge graph - a graphical representation of real-world entities and the links between them. The general intuition is that words which are closely linked on a knowledge graph probably have similar sentiment polarities. For this project, sentiments were generated based on English sentiment lexicons.
from asent import lexicons
rated_words = lexicons.get("lexicon_it_chen_skiena_2014_v1.txt")
Ido
License: GNU 3.0
This sentiment lexicon were generated by Chen, Y., & Skiena, S. (2014) using by propagation using a knowledge graph - a graphical representation of real-world entities and the links between them. The general intuition is that words which are closely linked on a knowledge graph probably have similar sentiment polarities. For this project, sentiments were generated based on English sentiment lexicons.
from asent import lexicons
rated_words = lexicons.get("lexicon_io_chen_skiena_2014_v1.txt")
Indonesian
License: GNU 3.0
This sentiment lexicon were generated by Chen, Y., & Skiena, S. (2014) using by propagation using a knowledge graph - a graphical representation of real-world entities and the links between them. The general intuition is that words which are closely linked on a knowledge graph probably have similar sentiment polarities. For this project, sentiments were generated based on English sentiment lexicons.
from asent import lexicons
rated_words = lexicons.get("lexicon_id_chen_skiena_2014_v1.txt")
Interlingua
License: GNU 3.0
This sentiment lexicon were generated by Chen, Y., & Skiena, S. (2014) using by propagation using a knowledge graph - a graphical representation of real-world entities and the links between them. The general intuition is that words which are closely linked on a knowledge graph probably have similar sentiment polarities. For this project, sentiments were generated based on English sentiment lexicons.
from asent import lexicons
rated_words = lexicons.get("lexicon_ia_chen_skiena_2014_v1.txt")
Irish
License: GNU 3.0
This sentiment lexicon were generated by Chen, Y., & Skiena, S. (2014) using by propagation using a knowledge graph - a graphical representation of real-world entities and the links between them. The general intuition is that words which are closely linked on a knowledge graph probably have similar sentiment polarities. For this project, sentiments were generated based on English sentiment lexicons.
from asent import lexicons
rated_words = lexicons.get("lexicon_ga_chen_skiena_2014_v1.txt")
Kannada
License: GNU 3.0
This sentiment lexicon were generated by Chen, Y., & Skiena, S. (2014) using by propagation using a knowledge graph - a graphical representation of real-world entities and the links between them. The general intuition is that words which are closely linked on a knowledge graph probably have similar sentiment polarities. For this project, sentiments were generated based on English sentiment lexicons.
from asent import lexicons
rated_words = lexicons.get("lexicon_kn_chen_skiena_2014_v1.txt")
Khmer
License: GNU 3.0
This sentiment lexicon were generated by Chen, Y., & Skiena, S. (2014) using by propagation using a knowledge graph - a graphical representation of real-world entities and the links between them. The general intuition is that words which are closely linked on a knowledge graph probably have similar sentiment polarities. For this project, sentiments were generated based on English sentiment lexicons.
from asent import lexicons
rated_words = lexicons.get("lexicon_km_chen_skiena_2014_v1.txt")
Kirghiz, Kyrgyz
License: GNU 3.0
This sentiment lexicon were generated by Chen, Y., & Skiena, S. (2014) using by propagation using a knowledge graph - a graphical representation of real-world entities and the links between them. The general intuition is that words which are closely linked on a knowledge graph probably have similar sentiment polarities. For this project, sentiments were generated based on English sentiment lexicons.
from asent import lexicons
rated_words = lexicons.get("lexicon_ky_chen_skiena_2014_v1.txt")
Korean
License: GNU 3.0
This sentiment lexicon were generated by Chen, Y., & Skiena, S. (2014) using by propagation using a knowledge graph - a graphical representation of real-world entities and the links between them. The general intuition is that words which are closely linked on a knowledge graph probably have similar sentiment polarities. For this project, sentiments were generated based on English sentiment lexicons.
from asent import lexicons
rated_words = lexicons.get("lexicon_ko_chen_skiena_2014_v1.txt")
Kurdish
License: GNU 3.0
This sentiment lexicon were generated by Chen, Y., & Skiena, S. (2014) using by propagation using a knowledge graph - a graphical representation of real-world entities and the links between them. The general intuition is that words which are closely linked on a knowledge graph probably have similar sentiment polarities. For this project, sentiments were generated based on English sentiment lexicons.
from asent import lexicons
rated_words = lexicons.get("lexicon_ku_chen_skiena_2014_v1.txt")
Latin
License: GNU 3.0
This sentiment lexicon were generated by Chen, Y., & Skiena, S. (2014) using by propagation using a knowledge graph - a graphical representation of real-world entities and the links between them. The general intuition is that words which are closely linked on a knowledge graph probably have similar sentiment polarities. For this project, sentiments were generated based on English sentiment lexicons.
from asent import lexicons
rated_words = lexicons.get("lexicon_la_chen_skiena_2014_v1.txt")
Latvian
License: GNU 3.0
This sentiment lexicon were generated by Chen, Y., & Skiena, S. (2014) using by propagation using a knowledge graph - a graphical representation of real-world entities and the links between them. The general intuition is that words which are closely linked on a knowledge graph probably have similar sentiment polarities. For this project, sentiments were generated based on English sentiment lexicons.
from asent import lexicons
rated_words = lexicons.get("lexicon_lv_chen_skiena_2014_v1.txt")
Lithuanian
License: GNU 3.0
This sentiment lexicon were generated by Chen, Y., & Skiena, S. (2014) using by propagation using a knowledge graph - a graphical representation of real-world entities and the links between them. The general intuition is that words which are closely linked on a knowledge graph probably have similar sentiment polarities. For this project, sentiments were generated based on English sentiment lexicons.
from asent import lexicons
rated_words = lexicons.get("lexicon_lt_chen_skiena_2014_v1.txt")
Luxembourgish
License: GNU 3.0
This sentiment lexicon were generated by Chen, Y., & Skiena, S. (2014) using by propagation using a knowledge graph - a graphical representation of real-world entities and the links between them. The general intuition is that words which are closely linked on a knowledge graph probably have similar sentiment polarities. For this project, sentiments were generated based on English sentiment lexicons.
from asent import lexicons
rated_words = lexicons.get("lexicon_lb_chen_skiena_2014_v1.txt")
Luxembourgish
License: GNU 3.0
This sentiment lexicon were generated by Chen, Y., & Skiena, S. (2014) using by propagation using a knowledge graph - a graphical representation of real-world entities and the links between them. The general intuition is that words which are closely linked on a knowledge graph probably have similar sentiment polarities. For this project, sentiments were generated based on English sentiment lexicons.
from asent import lexicons
rated_words = lexicons.get("lexicon_lb_chen_skiena_2014_v1.txt")
Macedonian
License: GNU 3.0
This sentiment lexicon were generated by Chen, Y., & Skiena, S. (2014) using by propagation using a knowledge graph - a graphical representation of real-world entities and the links between them. The general intuition is that words which are closely linked on a knowledge graph probably have similar sentiment polarities. For this project, sentiments were generated based on English sentiment lexicons.
from asent import lexicons
rated_words = lexicons.get("lexicon_mk_chen_skiena_2014_v1.txt")
Macedonian
License: GNU 3.0
This sentiment lexicon were generated by Chen, Y., & Skiena, S. (2014) using by propagation using a knowledge graph - a graphical representation of real-world entities and the links between them. The general intuition is that words which are closely linked on a knowledge graph probably have similar sentiment polarities. For this project, sentiments were generated based on English sentiment lexicons.
from asent import lexicons
rated_words = lexicons.get("lexicon_mk_chen_skiena_2014_v1.txt")
Malay
License: GNU 3.0
This sentiment lexicon were generated by Chen, Y., & Skiena, S. (2014) using by propagation using a knowledge graph - a graphical representation of real-world entities and the links between them. The general intuition is that words which are closely linked on a knowledge graph probably have similar sentiment polarities. For this project, sentiments were generated based on English sentiment lexicons.
from asent import lexicons
rated_words = lexicons.get("lexicon_ms_chen_skiena_2014_v1.txt")
Malay
License: GNU 3.0
This sentiment lexicon were generated by Chen, Y., & Skiena, S. (2014) using by propagation using a knowledge graph - a graphical representation of real-world entities and the links between them. The general intuition is that words which are closely linked on a knowledge graph probably have similar sentiment polarities. For this project, sentiments were generated based on English sentiment lexicons.
from asent import lexicons
rated_words = lexicons.get("lexicon_ms_chen_skiena_2014_v1.txt")
Maltese
License: GNU 3.0
This sentiment lexicon were generated by Chen, Y., & Skiena, S. (2014) using by propagation using a knowledge graph - a graphical representation of real-world entities and the links between them. The general intuition is that words which are closely linked on a knowledge graph probably have similar sentiment polarities. For this project, sentiments were generated based on English sentiment lexicons.
from asent import lexicons
rated_words = lexicons.get("lexicon_mt_chen_skiena_2014_v1.txt")
Marathi
License: GNU 3.0
This sentiment lexicon were generated by Chen, Y., & Skiena, S. (2014) using by propagation using a knowledge graph - a graphical representation of real-world entities and the links between them. The general intuition is that words which are closely linked on a knowledge graph probably have similar sentiment polarities. For this project, sentiments were generated based on English sentiment lexicons.
from asent import lexicons
rated_words = lexicons.get("lexicon_mr_chen_skiena_2014_v1.txt")
Norwegian Nynorsk
License: GNU 3.0
This sentiment lexicon were generated by Chen, Y., & Skiena, S. (2014) using by propagation using a knowledge graph - a graphical representation of real-world entities and the links between them. The general intuition is that words which are closely linked on a knowledge graph probably have similar sentiment polarities. For this project, sentiments were generated based on English sentiment lexicons.
from asent import lexicons
rated_words = lexicons.get("lexicon_nn_chen_skiena_2014_v1.txt")
Persian
License: GNU 3.0
This sentiment lexicon were generated by Chen, Y., & Skiena, S. (2014) using by propagation using a knowledge graph - a graphical representation of real-world entities and the links between them. The general intuition is that words which are closely linked on a knowledge graph probably have similar sentiment polarities. For this project, sentiments were generated based on English sentiment lexicons.
from asent import lexicons
rated_words = lexicons.get("lexicon_fa_chen_skiena_2014_v1.txt")
Polish
License: GNU 3.0
This sentiment lexicon were generated by Chen, Y., & Skiena, S. (2014) using by propagation using a knowledge graph - a graphical representation of real-world entities and the links between them. The general intuition is that words which are closely linked on a knowledge graph probably have similar sentiment polarities. For this project, sentiments were generated based on English sentiment lexicons.
from asent import lexicons
rated_words = lexicons.get("lexicon_pl_chen_skiena_2014_v1.txt")
Portuguese
License: GNU 3.0
This sentiment lexicon were generated by Chen, Y., & Skiena, S. (2014) using by propagation using a knowledge graph - a graphical representation of real-world entities and the links between them. The general intuition is that words which are closely linked on a knowledge graph probably have similar sentiment polarities. For this project, sentiments were generated based on English sentiment lexicons.
from asent import lexicons
rated_words = lexicons.get("lexicon_pt_chen_skiena_2014_v1.txt")
Romanian, Moldavian
License: GNU 3.0
This sentiment lexicon were generated by Chen, Y., & Skiena, S. (2014) using by propagation using a knowledge graph - a graphical representation of real-world entities and the links between them. The general intuition is that words which are closely linked on a knowledge graph probably have similar sentiment polarities. For this project, sentiments were generated based on English sentiment lexicons.
from asent import lexicons
rated_words = lexicons.get("lexicon_ro_chen_skiena_2014_v1.txt")
Romansh
License: GNU 3.0
This sentiment lexicon were generated by Chen, Y., & Skiena, S. (2014) using by propagation using a knowledge graph - a graphical representation of real-world entities and the links between them. The general intuition is that words which are closely linked on a knowledge graph probably have similar sentiment polarities. For this project, sentiments were generated based on English sentiment lexicons.
from asent import lexicons
rated_words = lexicons.get("lexicon_rm_chen_skiena_2014_v1.txt")
Romansh
License: GNU 3.0
This sentiment lexicon were generated by Chen, Y., & Skiena, S. (2014) using by propagation using a knowledge graph - a graphical representation of real-world entities and the links between them. The general intuition is that words which are closely linked on a knowledge graph probably have similar sentiment polarities. For this project, sentiments were generated based on English sentiment lexicons.
from asent import lexicons
rated_words = lexicons.get("lexicon_rm_chen_skiena_2014_v1.txt")
Russian
License: GNU 3.0
This sentiment lexicon were generated by Chen, Y., & Skiena, S. (2014) using by propagation using a knowledge graph - a graphical representation of real-world entities and the links between them. The general intuition is that words which are closely linked on a knowledge graph probably have similar sentiment polarities. For this project, sentiments were generated based on English sentiment lexicons.
from asent import lexicons
rated_words = lexicons.get("lexicon_ru_chen_skiena_2014_v1.txt")
Scottish Gaelic
License: GNU 3.0
This sentiment lexicon were generated by Chen, Y., & Skiena, S. (2014) using by propagation using a knowledge graph - a graphical representation of real-world entities and the links between them. The general intuition is that words which are closely linked on a knowledge graph probably have similar sentiment polarities. For this project, sentiments were generated based on English sentiment lexicons.
from asent import lexicons
rated_words = lexicons.get("lexicon_gd_chen_skiena_2014_v1.txt")
Scottish Gaelic
License: GNU 3.0
This sentiment lexicon were generated by Chen, Y., & Skiena, S. (2014) using by propagation using a knowledge graph - a graphical representation of real-world entities and the links between them. The general intuition is that words which are closely linked on a knowledge graph probably have similar sentiment polarities. For this project, sentiments were generated based on English sentiment lexicons.
from asent import lexicons
rated_words = lexicons.get("lexicon_gd_chen_skiena_2014_v1.txt")
Serbian
License: GNU 3.0
This sentiment lexicon were generated by Chen, Y., & Skiena, S. (2014) using by propagation using a knowledge graph - a graphical representation of real-world entities and the links between them. The general intuition is that words which are closely linked on a knowledge graph probably have similar sentiment polarities. For this project, sentiments were generated based on English sentiment lexicons.
from asent import lexicons
rated_words = lexicons.get("lexicon_sr_chen_skiena_2014_v1.txt")
Slovak
License: GNU 3.0
This sentiment lexicon were generated by Chen, Y., & Skiena, S. (2014) using by propagation using a knowledge graph - a graphical representation of real-world entities and the links between them. The general intuition is that words which are closely linked on a knowledge graph probably have similar sentiment polarities. For this project, sentiments were generated based on English sentiment lexicons.
from asent import lexicons
rated_words = lexicons.get("lexicon_sk_chen_skiena_2014_v1.txt")
Slovene
License: GNU 3.0
This sentiment lexicon were generated by Chen, Y., & Skiena, S. (2014) using by propagation using a knowledge graph - a graphical representation of real-world entities and the links between them. The general intuition is that words which are closely linked on a knowledge graph probably have similar sentiment polarities. For this project, sentiments were generated based on English sentiment lexicons.
from asent import lexicons
rated_words = lexicons.get("lexicon_sl_chen_skiena_2014_v1.txt")
Spanish, Castilian
License: GNU 3.0
This sentiment lexicon were generated by Chen, Y., & Skiena, S. (2014) using by propagation using a knowledge graph - a graphical representation of real-world entities and the links between them. The general intuition is that words which are closely linked on a knowledge graph probably have similar sentiment polarities. For this project, sentiments were generated based on English sentiment lexicons.
from asent import lexicons
rated_words = lexicons.get("lexicon_es_chen_skiena_2014_v1.txt")
Swahili
License: GNU 3.0
This sentiment lexicon were generated by Chen, Y., & Skiena, S. (2014) using by propagation using a knowledge graph - a graphical representation of real-world entities and the links between them. The general intuition is that words which are closely linked on a knowledge graph probably have similar sentiment polarities. For this project, sentiments were generated based on English sentiment lexicons.
from asent import lexicons
rated_words = lexicons.get("lexicon_sw_chen_skiena_2014_v1.txt")
Tamil
License: GNU 3.0
This sentiment lexicon were generated by Chen, Y., & Skiena, S. (2014) using by propagation using a knowledge graph - a graphical representation of real-world entities and the links between them. The general intuition is that words which are closely linked on a knowledge graph probably have similar sentiment polarities. For this project, sentiments were generated based on English sentiment lexicons.
from asent import lexicons
rated_words = lexicons.get("lexicon_ta_chen_skiena_2014_v1.txt")
Tagalog
License: GNU 3.0
This sentiment lexicon were generated by Chen, Y., & Skiena, S. (2014) using by propagation using a knowledge graph - a graphical representation of real-world entities and the links between them. The general intuition is that words which are closely linked on a knowledge graph probably have similar sentiment polarities. For this project, sentiments were generated based on English sentiment lexicons.
from asent import lexicons
rated_words = lexicons.get("lexicon_tl_chen_skiena_2014_v1.txt")
Telugu
License: GNU 3.0
This sentiment lexicon were generated by Chen, Y., & Skiena, S. (2014) using by propagation using a knowledge graph - a graphical representation of real-world entities and the links between them. The general intuition is that words which are closely linked on a knowledge graph probably have similar sentiment polarities. For this project, sentiments were generated based on English sentiment lexicons.
from asent import lexicons
rated_words = lexicons.get("lexicon_te_chen_skiena_2014_v1.txt")
Thai
License: GNU 3.0
This sentiment lexicon were generated by Chen, Y., & Skiena, S. (2014) using by propagation using a knowledge graph - a graphical representation of real-world entities and the links between them. The general intuition is that words which are closely linked on a knowledge graph probably have similar sentiment polarities. For this project, sentiments were generated based on English sentiment lexicons.
from asent import lexicons
rated_words = lexicons.get("lexicon_th_chen_skiena_2014_v1.txt")
Turkmen
License: GNU 3.0
This sentiment lexicon were generated by Chen, Y., & Skiena, S. (2014) using by propagation using a knowledge graph - a graphical representation of real-world entities and the links between them. The general intuition is that words which are closely linked on a knowledge graph probably have similar sentiment polarities. For this project, sentiments were generated based on English sentiment lexicons.
from asent import lexicons
rated_words = lexicons.get("lexicon_tk_chen_skiena_2014_v1.txt")
Turkish
License: GNU 3.0
This sentiment lexicon were generated by Chen, Y., & Skiena, S. (2014) using by propagation using a knowledge graph - a graphical representation of real-world entities and the links between them. The general intuition is that words which are closely linked on a knowledge graph probably have similar sentiment polarities. For this project, sentiments were generated based on English sentiment lexicons.
from asent import lexicons
rated_words = lexicons.get("lexicon_tr_chen_skiena_2014_v1.txt")
Ukrainian
License: GNU 3.0
This sentiment lexicon were generated by Chen, Y., & Skiena, S. (2014) using by propagation using a knowledge graph - a graphical representation of real-world entities and the links between them. The general intuition is that words which are closely linked on a knowledge graph probably have similar sentiment polarities. For this project, sentiments were generated based on English sentiment lexicons.
from asent import lexicons
rated_words = lexicons.get("lexicon_uk_chen_skiena_2014_v1.txt")
Urdu
License: GNU 3.0
This sentiment lexicon were generated by Chen, Y., & Skiena, S. (2014) using by propagation using a knowledge graph - a graphical representation of real-world entities and the links between them. The general intuition is that words which are closely linked on a knowledge graph probably have similar sentiment polarities. For this project, sentiments were generated based on English sentiment lexicons.
from asent import lexicons
rated_words = lexicons.get("lexicon_ur_chen_skiena_2014_v1.txt")
Uzbek
License: GNU 3.0
This sentiment lexicon were generated by Chen, Y., & Skiena, S. (2014) using by propagation using a knowledge graph - a graphical representation of real-world entities and the links between them. The general intuition is that words which are closely linked on a knowledge graph probably have similar sentiment polarities. For this project, sentiments were generated based on English sentiment lexicons.
from asent import lexicons
rated_words = lexicons.get("lexicon_uz_chen_skiena_2014_v1.txt")
Vietnamese
License: GNU 3.0
This sentiment lexicon were generated by Chen, Y., & Skiena, S. (2014) using by propagation using a knowledge graph - a graphical representation of real-world entities and the links between them. The general intuition is that words which are closely linked on a knowledge graph probably have similar sentiment polarities. For this project, sentiments were generated based on English sentiment lexicons.
from asent import lexicons
rated_words = lexicons.get("lexicon_vi_chen_skiena_2014_v1.txt")
Volapük
License: GNU 3.0
This sentiment lexicon were generated by Chen, Y., & Skiena, S. (2014) using by propagation using a knowledge graph - a graphical representation of real-world entities and the links between them. The general intuition is that words which are closely linked on a knowledge graph probably have similar sentiment polarities. For this project, sentiments were generated based on English sentiment lexicons.
from asent import lexicons
rated_words = lexicons.get("lexicon_vo_chen_skiena_2014_v1.txt")
Walloon
License: GNU 3.0
This sentiment lexicon were generated by Chen, Y., & Skiena, S. (2014) using by propagation using a knowledge graph - a graphical representation of real-world entities and the links between them. The general intuition is that words which are closely linked on a knowledge graph probably have similar sentiment polarities. For this project, sentiments were generated based on English sentiment lexicons.
from asent import lexicons
rated_words = lexicons.get("lexicon_wa_chen_skiena_2014_v1.txt")
Welsh
License: GNU 3.0
This sentiment lexicon were generated by Chen, Y., & Skiena, S. (2014) using by propagation using a knowledge graph - a graphical representation of real-world entities and the links between them. The general intuition is that words which are closely linked on a knowledge graph probably have similar sentiment polarities. For this project, sentiments were generated based on English sentiment lexicons.
from asent import lexicons
rated_words = lexicons.get("lexicon_cy_chen_skiena_2014_v1.txt")
Western Frisian
License: GNU 3.0
This sentiment lexicon were generated by Chen, Y., & Skiena, S. (2014) using by propagation using a knowledge graph - a graphical representation of real-world entities and the links between them. The general intuition is that words which are closely linked on a knowledge graph probably have similar sentiment polarities. For this project, sentiments were generated based on English sentiment lexicons.
from asent import lexicons
rated_words = lexicons.get("lexicon_fy_chen_skiena_2014_v1.txt")
Yiddish
License: GNU 3.0
This sentiment lexicon were generated by Chen, Y., & Skiena, S. (2014) using by propagation using a knowledge graph - a graphical representation of real-world entities and the links between them. The general intuition is that words which are closely linked on a knowledge graph probably have similar sentiment polarities. For this project, sentiments were generated based on English sentiment lexicons.
from asent import lexicons
rated_words = lexicons.get("lexicon_yi_chen_skiena_2014_v1.txt")