Sentiment analysis of Social Media Text-Emoticon Post with Machine learning Models Contribution Title

Abstract

Social Media is an arena in recent times for people to share their perspectives on a variety of topics. Most of the social interactions are through the Social Media. Though all the Online Social Networks allow users to express their views and opinions in many forms like audio, video, text etc, the most popular form of expression is text, Emoticons and Emojis. The work presented in this paper aims at detecting the sentiments expressed in the Social Media posts. The Machine Learning Models namely Bernoulli Bayes, Multinomial Bayes, Regression and SVM were implemented. All these models were trained and tested with Twitter Data sets. Users on Twitter express their opinions in the form of tweets with limited characters. Tweets also contain Emoticons and Emojis therefore Twitter data sets are best suited for the sentiment analysis. The effect of emoticons present in the tweet is also analyzed. The models are first trained only with the text and then they are trained with text and emoticon in the tweet. The performance of all the four models in both cases are tested and the results are presented in the paper.

Supervised machine learning models for depression sentiment analysis

“…Samsari et al ( 2022 ) made use of the Naïve Bayes classifier to classify the data as either positive, negative, or neutral. Jagadishwari et al ( 2021 ) made use of the Linear regression model as well as the Support Vector Machine (SMV) model, and these two models generated similar results. Ranganathan and Tzacheva ( 2019 ) used the Support Vector Machine LibLinear in an article titled Emotion Mining in Social Media Data.…”

“…Social media mining stems from data mining (Babu and Kanaga, 2022 ), which could also be considered as a stem of machine learning in various fields (Babu and Kanaga, 2022 ). Data mining can be considered the process of finding useful sets of data from larger pools of accessible data (Jagadishwari et al, 2021 ; Babu and Kanaga, 2022 ). In some studies, this is considered an automated process of uncovering knowledge, relationships, and patterns in interrelated sets of data (Babu and Kanaga, 2022 ).…”

“…There are several forms in which this data could be found. Text, image, and voice are three common forms of data that are mined during the social media mining process (Jagadishwari et al, 2021 ).…”

AI-Based Conversational Agents: A Scoping Review From Technologies to Future Directions

“…Some research has been done for different tasks using multimodal utilizing text and emojis in various domains. The studies undertaken for emotion classification using text, emojis and images modalities [61], sentiment prediction using text and emojis [62], emotion analysis from twitter data using emojis [63], sentiment analysis of social media using text, emoticons, emojis [64] [65], enhancing opinion mining using emojis [66] are a few examples of study in this field. Moreover, in most research articles, deep learning methods has been employed for different tasks.…”