Twitter based sentiment analysis to predict public emotions using machine learning algorithms

Mohana, Rajni; Kalaiselvi, S.; Kousalya, K.; P, Mohamed Hanif; Lohappriya, D; K, Khalid Ali Khan

doi:10.1109/icirca51532.2021.9544817

Cited by 6 publications

(3 citation statements)

References 15 publications

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“…A significant increase was observed in performance after applying the N-gram feature extraction technique. In another research work, the authors used two classification methods: unigrams and bigrams, and attempts were made to include bigrams in vectors to improve accuracy [17]. Once removed, the function returns as a small or dense vector.…”

Section: Related Workmentioning

confidence: 99%

A Feature Extraction based Improved Sentiment Analysis on Apache Spark for Real-time Twitter Data

Kanungo,

Singh

2023

SCPE

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This paper aims to improve the accuracy of sentiment analysis on Apache Spark for a real-time general twitter data. A lot of works exist on sentiment analysis on offline or stored twitter data that uses several classification algorithms on relevant features extracted using well-known feature extraction methodologies on pre-processed text data. However, not much works exist for sentiment analysis of real-time twitter data and especially for the generic data on big data processing platforms such as Apache Spark. This paper proposes a real-time sentiment analysis for generic twitter data through Apache Spark using six classification algorithms on N-gram and Term Frequency – Inverse Document Frequency (TF-IDF) feature extraction methodologies on the pre-processed data. An exhaustive comparison is done using Logistic Regression (LR), Multinomial Naive Bayes (MNB), Random Forest Classfier (RFC), Support Vector Machine (SVM), K-Nearest Neighbour (K-NN), and Decision Tree (DT) classification algorithms. It is observed that the trigram feature extraction method performs the best on LR and SVM and the RFC results are also comparable on the considered general tweets data.

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Section: Related Workmentioning

confidence: 99%

A Feature Extraction based Improved Sentiment Analysis on Apache Spark for Real-time Twitter Data

Kanungo,

Singh

2023

SCPE

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“…The user's tweets are analyzed using Ekman emotional scale, which uses three variants measures and deployed community modularity detection technique. After more than a year of adjusting to distance learning techniques that are now thought of as the new norm, Mohana et al [28] constructed an opinion mining on the education level of Filipinos. They employed three distinct classification methods to assess opinion mining's accuracy.…”

Section: Related Workmentioning

confidence: 99%

A Perspective Study on Tweet Sentiment Analysis using Data Mining, Machine Learning and Deep Learning Paradigms

2023

IJFMR

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In recent years there has been an increase in interest in collecting and studying text from social networks, review websites, blogs, forums and other forms of user-generated information. The text offers a vast array of ideas from people of diverse profiles, including education, age and their perspectives, region of residence, on how they see goods and services, policy opinions, etc. The analysis of judgments, responses, and emotions drawn from texts is known as sentiment analysis. The sentiment categorization procedure establishes whether a text is subjective or objective, or whether it provokes both positive and negative responses. The most popular method of classification is based on polarity or orientation for accomplishing tweet sentiment analysis. In this paper, a detailed survey on various algorithms used for performing opinion mining, sentiment analysis, tweet sentiment analysis is discussed in detail. The study shows that text preprocessing, data mining, machine learning algorithm and deep learning paradigms plays a vital role in categorization of people’s feeling on a specific topic or a product. In this study, the existing challenges in optimizing the process of tweet sentiment analysis is also discussed and the suggestions for improving is also discussed.

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“…In conclusion, the findings of the research demonstrate that Deep Learning techniques are superior than other methodologies. [22]. (Aziz & Dimililer, 2020) study presents a framework for doing sentiment analysis using an ensemble of classifiers.…”

Section: ) Deep Auto Encoder (Dae)mentioning

confidence: 99%

A Review on Emotion and Sentiment analysis Using Learning Techniques

Jain¹,

Parmar²

2022

IJRASET

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Social media sites have become vital instruments for spreading ones personal emotions to a rest of the world, thanks to the rapid growth of the Internet. Writing, photos, audio, and video are all used by many people to express their opinions or points of view. Every second, a massive amount of unstructured data are generated on the Internet as a consequence of social networking sites. To understand human psychology, data must be processed as soon as it is generated, which can be done via sentiment classification, which detects polarity in texts. It establishes if the writer has a negative, positive, or neutral attitude forward towards a specific item, administration, person, or region. Sentiment analysis is insufficient in some cases, needing emotion detection, which appropriately measures a person's emotional/mental state. This review investigates sentiment analysis levels, different emotion models, and sentiment analysis techniques such as emotion detection. Furthermore, the issues faced during sentiment or emotion analysis are addressed in this study. A range of machine learning techniques methods to assessing sentiment were also explored

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Twitter based sentiment analysis to predict public emotions using machine learning algorithms

Cited by 6 publications

References 15 publications

A Feature Extraction based Improved Sentiment Analysis on Apache Spark for Real-time Twitter Data

A Feature Extraction based Improved Sentiment Analysis on Apache Spark for Real-time Twitter Data

A Perspective Study on Tweet Sentiment Analysis using Data Mining, Machine Learning and Deep Learning Paradigms

A Review on Emotion and Sentiment analysis Using Learning Techniques

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