Sentiment Analysis Using Multinomial LR-LSTM Model

Abstract

Sentiment analysis (SA) refers to a technique utilized to ascertain the emotional state conveyed in information or text. It involves categorizing the text into three classes: positive, negative, or neutral. For instance, when someone says "the aqi of the city is good," they are expressing a positive opinion about the aqi of a specific place, while the statement "the aqi is bad" reflects the opposite. The introduction of social media increased the amount of content on the internet of sentiment data. Users on various social media platforms have been able to offer their opinions on various products, services, etc. These opinions are often expressed on social media in the form of movie reviews, product reviews, user comments, posts, etc. In light of this context, one of the captivating research areas in Natural Language Processing (NLP) is Twitter sentiment analysis. The paper proposes a stacked Multinomial-LR-LSTM model for the classification of tweets into three classes. Tweets are re-annotated using Text Blob. Twitter Sentiment dataset was used for experiments with accuracy of 97%.