Exploring Sentiment in Tweets: An Ordinal Regression Analysis

The fundamental goal of sentiment analysis is to find and categorize any views or feelings that are communicated in a text. Nowadays, discussing thoughts and expressing feelings through social networking sites is widespread. Consequently, a vast amount of data is generated every day, which can be mined successfully to extract valuable information. Performing sentiment analysis on such data can be useful for producing an aggregated view of particular products. Due to the prevalence of slang and misspellings, sentiment analysis on Twitter is frequently a challenging undertaking. Additionally, we are constantly exposed to new terms, which makes it more difficult to assess and compute the sentiment compared to traditional sentiment analysis. Twitter limits a tweet's length to 140 characters. Consequently, obtaining important information from brief messages is another obstacle. Knowledge-based approaches and machine learning can significantly contribute to the sentiment analysis of tweets. The amount of data produced by people, i.e., users of a certain social site, is growing exponentially as a result of changing behavior of various types of networking sites like Snapchat, Instagram, Twitter, etc. The purpose of this paper is to determine the emotions underlying these posts. We have decided to use Twitter as our platform for this. In this study, we investigate the views expressed by Twitter users concerning certain companies. By computing a basic sentiment score and then categorizing them as positive or negative, the corporation would be provided with critical feedback about its products from individuals around the world. The proposed LSTM model has proved to be 93% efficient in comparison with previous models which were accurate up to 86%.