2,021 research outputs found
Identifying Emotions in Social Media: Comparison of Word-emotion lexica
In recent years, emotions expressed in social media messages have become a vivid research topic due to their influence on the spread of misinformation and online radicalization over online social networks. Thus, it is important to correctly identify emotions in order to make inferences from social media messages. In this paper, we report on the performance of three publicly available word-emotion lexicons (NRC, DepecheMood, EmoSenticNet) over a set of Facebook and Twitter messages. To this end, we designed and implemented an algorithm that applies natural language processing (NLP) techniques along with a number of heuristics that reflect the way humans naturally assess emotions in written texts. In order to evaluate the appropriateness of the obtained emotion scores, we conducted a questionnaire-based survey with human raters. Our results show that there are noticeable differences between the performance of the lexicons as well as with respect to emotion scores the human raters provided in our surve
Large-Scale Goodness Polarity Lexicons for Community Question Answering
We transfer a key idea from the field of sentiment analysis to a new domain:
community question answering (cQA). The cQA task we are interested in is the
following: given a question and a thread of comments, we want to re-rank the
comments so that the ones that are good answers to the question would be ranked
higher than the bad ones. We notice that good vs. bad comments use specific
vocabulary and that one can often predict the goodness/badness of a comment
even ignoring the question, based on the comment contents only. This leads us
to the idea to build a good/bad polarity lexicon as an analogy to the
positive/negative sentiment polarity lexicons, commonly used in sentiment
analysis. In particular, we use pointwise mutual information in order to build
large-scale goodness polarity lexicons in a semi-supervised manner starting
with a small number of initial seeds. The evaluation results show an
improvement of 0.7 MAP points absolute over a very strong baseline and
state-of-the art performance on SemEval-2016 Task 3.Comment: SIGIR '17, August 07-11, 2017, Shinjuku, Tokyo, Japan; Community
Question Answering; Goodness polarity lexicons; Sentiment Analysi
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