Twitter Sentiment Analysis and Emotion Detection Using NLTK and TextBlob

Title

Twitter Sentiment Analysis and Emotion Detection Using NLTK and TextBlob

Creator

Nehal; Jeet D.; Sharma V.; Mishra S.; Iwendi C.; Osamor J.

Description

On an average, approximately 7000 tweets are communicated each second and in total it piles up to around 300 billion tweets every year. Society are free to contribute their opinions on public platform and hence it acts as a reliable interface to assess society ongoing viewpoint and attitude over any matter or event. Consumers very often make use of social media to exchange their views about anything. Business may get domain for enhancement and smooth interpretation of the behavior of people regarding various facts through opinion mining. Thus to carry out this mining of opinions on social media interface, textual categorization with language analysis is of great help. With the help of NLP token tool, phrases can be divided into various word series after dropping stop phrases. Larger tweets tokenizing and classifying into distinct labels is a concern. Thus, the main objective of this framework is to process the tweets based on specific keywords given by user, categorize these phrases into negative, positive and neutral ones. TextBlob module assists users and developers to interpret user sentiments about a news. This research tries to give suggestion a textual opinion assessment on social media samples utilizing the NLTK and TextBlob modules. 2023 IEEE.

Source

2023 4th International Conference on Computation, Automation and Knowledge Management, ICCAKM 2023

Date

2023-01-01

Publisher

Institute of Electrical and Electronics Engineers Inc.

Subject

Natural Language Processing; NLTK; Polarity; Python; Sentiment Analysis; Subjectivity; TextBlob; Tokenization

Coverage

Nehal, Kalinga Institute of Industrial Technology, India; Jeet D., Kalinga Institute of Industrial Technology, India; Sharma V., Christ (Deemed to Be University), Department of Computational Sciences, Delhi NCR, India; Mishra S., Kalinga Institute of Industrial Technology, India; Iwendi C., School of Creative Technologies, University of Bolton, United Kingdom; Osamor J., Glasgow Caledonian University, Department of Cybersecurity and Networks, United Kingdom

Rights

All Open Access; Green Open Access

Relation

ISBN: 979-835039324-8

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1109/ICCAKM58659.2023.10449540

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85187555014&doi=10.1109%2fICCAKM58659.2023.10449540&partnerID=40&md5=31da5f71cb563776942cc4fe356e7283