Machine Learning-Based Sentiment Analysis for Twitter Accounts

Abstract

Growth in the area of opinion mining and sentiment analysis has been rapid and aims to explore the opinions or text present on different platforms of social media through machine-learning techniques with sentiment, subjectivity analysis or polarity calculations. Despite the use of various machine-learning techniques and tools for sentiment analysis during elections, there is a dire need for a state-of-the-art approach. To deal with these challenges, the contribution of this paper includes the adoption of a hybrid approach that involves a sentiment analyzer that includes machine learning. Moreover, this paper also provides a comparison of techniques of sentiment analysis in the analysis of political views by applying supervised machine-learning algorithms such as Naïve Bayes and support vector machines (SVM).

A Novel Method for Twitter Sentiment Analysis Based on Attentional-Graph Neural Network

“…For the retained-word count f , optimal value locates between 2 6 to 2 8 . While for the user embedding length f , we suggest a value between 2 4 to 2 5 . Later in the word-embedding method experiments, Skip-Gram shows potent power over CBOW and pure autoencoder in our scenario, which make it empirically the best choice among these three methods.…”

“…Recent research from Hasan [5] provides us with an integrated approach based on sentiment analyzer and machine learning. For the sentiment analyzer, the author considered the TextBlob [9], a customized Word Sense Disambiguation (W-WSD) [10] and The SentiWordNet [11] for comparison.…”

“…Traditional methods [2,3,5] based on machine learning take the tweet's text messages as the input. These works usually use statistical methods to extract features for later analysis, and they are able to achieve decent results (generally above 80% in accuracy).…”

Sentiment Classification Using Convolutional Neural Networks

“…The definition of sentiment labels will affect the outcome of the sentiment analysis, so we need to define the sentiment labels carefully. There have been studies that defined three or more sentiment labels (e.g., opinion rating scores, emotional feelings) [9][10][11][12][13], and some studies adopted two-dimensional labels (e.g., positive and negative) [14][15][16][17][18][19][20][21][22][23][24][25][26]. Although there has been much performance improvement in the field of sentiment analysis, the binary classification of sentiment still remains challenging; for example, the performance (e.g., accuracy) of recent studies varied from 70-90% in terms of the characteristics of data.…”

Who Wins the Game of Thrones? How Sentiments Improve the Prediction of Candidate Choice

“…The empirical study used Amazon Mturk to acquire a sample of 1279 participants in the last week (13)(14)(15)(16)(17)(18)(19) May 2019) before the last Game of Thrones episode aired on television. For the data collection, an online questionnaire was answered by 1279 participants.…”