Sentiment Analysis of Twitter Data Through Machine Learning Techniques

Abstract In this article, an innovative approach to perform the sentiment analysis (SA) has been presented. The proposed system handles the issues of Romanized or abbreviated text and spelling variations in the text to perform the sentiment analysis. The training data set of 3,000 movie reviews and tweets has been manually labeled by native speakers of Hindi in three classes, i.e. positive, negative, and neutral. The system uses WEKA (Waikato Environment for Knowledge Analysis) tool to convert these string data into numerical matrices and applies three machine learning techniques, i.e. Naive Bayes (NB), J48, and support vector machine (SVM). The proposed system has been tested on 100 movie reviews and tweets, and it has been observed that SVM has performed best in comparison to other classifiers, and it has an accuracy of 68% for movie reviews and 82% in case of tweets. The results of the proposed system are very promising and can be used in emerging applications like SA of product reviews and social media analysis. Additionally, the proposed system can be used in other cultural/social benefits like predicting/fighting human riots.

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A Survey on Twitter Sentimental Analysis with Machine Learning Techniques

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i2.32.16268 ◽

2018 ◽

Vol 7 (2.32) ◽

pp. 462

Author(s):

G Krishna Chaitanya ◽

Dinesh Reddy Meka ◽

Vakalapudi Surya Vamsi ◽

M V S Ravi Karthik

Keyword(s):

Machine Learning ◽

Sentiment Analysis ◽

Language Processing ◽

Machine Learning Techniques ◽

Buying Behavior ◽

Online Retail ◽

User Reviews ◽

Business Plans ◽

Learning Techniques ◽

User Data

Sentiment or emotion behind a tweet from Twitter or a post from Facebook can help us answer what opinions or feedback a person has. With the advent of growing user-generated blogs, posts and reviews across various social media and online retails, calls for an understanding of these afore mentioned user data acts as a catalyst in building Recommender systems and drive business plans. User reviews on online retail stores influence buying behavior of customers and thus complements the ever-growing need of sentiment analysis. Machine Learning helps us to read between the lines of tweets by proving us with various algorithms like Naïve Bayes, SVM, etc. Sentiment Analysis uses Machine Learning and Natural Language Processing (NLP) to extract, classify and analyze tweets for sentiments (emotions). There are various packages and frameworks in R and Python that aid in Sentiment Analysis or Text Mining in general.

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Sentiment Analysis for Airline Tweets Utilizing Machine Learning Techniques

International Conference on Mobile Computing and Sustainable Informatics - EAI/Springer Innovations in Communication and Computing ◽

10.1007/978-3-030-49795-8_75 ◽

2020 ◽

pp. 791-799

Author(s):

G. Ravi Kumar ◽

K. Venkata Sheshanna ◽

G. Anjan Babu

Keyword(s):

Machine Learning ◽

Sentiment Analysis ◽

Machine Learning Techniques ◽

Learning Techniques

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Sentiment Analysis using various Machine Learning and Deep Learning Techniques

Journal of the Nigerian Society of Physical Sciences ◽

10.46481/jnsps.2021.308 ◽

2021 ◽

pp. 385-394

Author(s):

V Umarani ◽

A Julian ◽

J Deepa

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Sentiment Analysis ◽

Naive Bayes ◽

Naïve Bayes ◽

Supervised Machine Learning ◽

Machine Learning Techniques ◽

Support Vector ◽

Analysis Process ◽

Learning Techniques

Sentiment analysis has gained a lot of attention from researchers in the last year because it has been widely applied to a variety of application domains such as business, government, education, sports, tourism, biomedicine, and telecommunication services. Sentiment analysis is an automated computational method for studying or evaluating sentiments, feelings, and emotions expressed as comments, feedbacks, or critiques. The sentiment analysis process can be automated using machine learning techniques, which analyses text patterns faster. The supervised machine learning technique is the most used mechanism for sentiment analysis. The proposed work discusses the flow of sentiment analysis process and investigates the common supervised machine learning techniques such as multinomial naive bayes, Bernoulli naive bayes, logistic regression, support vector machine, random forest, K-nearest neighbor, decision tree, and deep learning techniques such as Long Short-Term Memory and Convolution Neural Network. The work examines such learning methods using standard data set and the experimental results of sentiment analysis demonstrate the performance of various classifiers taken in terms of the precision, recall, F1-score, RoC-Curve, accuracy, running time and k fold cross validation and helps in appreciating the novelty of the several deep learning techniques and also giving the user an overview of choosing the right technique for their application.

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Sentiment Analysis in Social Media using Machine Learning Techniques

Iraqi Journal of Science ◽

10.24996/ijs.2020.61.1.22 ◽

2020 ◽

pp. 193-201 ◽

Cited By ~ 1

Author(s):

Hayder A. Alatabi ◽

Ayad R. Abbas

Keyword(s):

Machine Learning ◽

Social Media ◽

Sentiment Analysis ◽

Machine Learning Techniques ◽

Great Success ◽

Social Media Data ◽

Learning Techniques ◽

The World ◽

Analysis System ◽

Media Data

Over the last period, social media achieved a widespread use worldwide where the statistics indicate that more than three billion people are on social media, leading to large quantities of data online. To analyze these large quantities of data, a special classification method known as sentiment analysis, is used. This paper presents a new sentiment analysis system based on machine learning techniques, which aims to create a process to extract the polarity from social media texts. By using machine learning techniques, sentiment analysis achieved a great success around the world. This paper investigates this topic and proposes a sentiment analysis system built on Bayesian Rough Decision Tree (BRDT) algorithm. The experimental results show the success of this system where the accuracy of the system is more than 95% on social media data.

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