5 Essential Papers on Sentiment Analysis

From virtual assistants to content moderation, sentiment analysis has a wide range of use cases. AI models that can recognize emotion and opinion have a myriad of applications in numerous industries. Therefore, there is a large growing interest in the creation of emotionally intelligent machines. The same can be said for the research being done in natural language processing (NLP). To highlight some of the work being done in the field, below are five essential papers on sentiment analysis and sentiment classification.

1. Deep Learning for Hate Speech Detection in Tweets

One of the most useful applications of sentiment classification models is the detection of hate speech. Recently, there have been numerous reports of the harsh lives of content moderation staff. With the advancement of automated hate speech detection and other content moderation models, hopefully human moderators filtering graphic content will no longer be necessary.

In this paper, the team defines their task of hate speech detection as classifying whether or not a particular Twitter post is racist, sexist, or neither. To do so, the researchers experiment on a dataset containing 16,000 tweets. Within the dataset, 1,972 of the tweets have been labeled as racist. 3,383 have been labeled as sexist. The remaining tweets have…