Version 1
: Received: 13 June 2019 / Approved: 14 June 2019 / Online: 14 June 2019 (14:55:52 CEST)
How to cite:
Kumar, S. LexScore: A Semantic Approach to Scoring Domain Specific Sentiment Lexicons. Preprints2019, 2019060133. https://doi.org/10.20944/preprints201906.0133.v1
Kumar, S. LexScore: A Semantic Approach to Scoring Domain Specific Sentiment Lexicons. Preprints 2019, 2019060133. https://doi.org/10.20944/preprints201906.0133.v1
Kumar, S. LexScore: A Semantic Approach to Scoring Domain Specific Sentiment Lexicons. Preprints2019, 2019060133. https://doi.org/10.20944/preprints201906.0133.v1
APA Style
Kumar, S. (2019). LexScore: A Semantic Approach to Scoring Domain Specific Sentiment Lexicons. Preprints. https://doi.org/10.20944/preprints201906.0133.v1
Chicago/Turabian Style
Kumar, S. 2019 "LexScore: A Semantic Approach to Scoring Domain Specific Sentiment Lexicons" Preprints. https://doi.org/10.20944/preprints201906.0133.v1
Abstract
The sentiment of a word varies based on its context of usage: the words used around it and the part-of-speech it is used as. This paper proposes a technique to suggest the sentiment of a word by combining its part-of-speech and the semantic similarities of its co-occurrences with both context-specific and pre-trained embeddings to achieve powerful and fast results. A study was conducted across domains and sub-domains to measure variance of sentiment by switching domains and switching context within the same domain. Re-scoring a commonly used polarity lexicon showed that 10% of words changed scores while switching domains and 8% changed scores within domains while switching context. Part of Speech analysis on 65,353 commonly used sentiment lexicons showed that 81% of sentiment bearing (non-neutral) lexicons were of the tags NN (Common Noun), JJ (Adjective) or NNS (Proper Noun).
Keywords
Natural Language Processing, Sentiment Analysis
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
