Sentiment analysis framework using data driven approach

Md Jahedul Islam, Md Shubiour Shuvo, Tonmoy Sarker, Mohammad Zavid Parvez, Md Anisur Rahman

Research output: Book chapter/Published conference paperConference paperpeer-review

Abstract

Internet is free and straightforward access to an immense measure of crude content information that can be mined for sentiment analysis. For a long time, this has been used for market research, user opinion mining, recommendation systems, analysis of people’s views on a topic. Many different techniques have been developed, yet much complication remains. Selecting and understanding attribute patterns in a text dataset is essential to build a good model and know where this model can be used. Different text datasets have different relations between their attributes and classes. For example, let us take a dataset with totally random English texts labeled as positive or negative. We expect that extracted attributes for the positive or negative class are very heavy with general words that we consider positive or negative in everyday English use. However, if the dataset is created on a niche topic, such as an economic pandemic, we would probably see that positive and negative classes are heavy with words specific to these topics, or they may not be considered the classifier. However, we might want to give importance to those niche-specific attributes specifically. In this paper, we take five different datasets of different instance lengths. We go through some attribute selection techniques and use them under some classifiers to visualize a pattern, do sentence-level sentiment analysis, and finally extract patterns from the datasets to analyze them. There are few related works on these datasets,and our technique performed better than the existing works.This paper aims to present a method that can easily be fruitful to any dataset for text mining and decent accuracy.
Original languageEnglish
Title of host publication2021 IEEE International Conference on Data Mining Workshops (ICDMW)
EditorsBing Xue, Mykola Pechenizkiy, Yun Sing Koh
PublisherIEEE Xplore
Pages143-150
Number of pages8
ISBN (Electronic)9781665424271
DOIs
Publication statusPublished - 2021
EventIEEE International Conference on Data Mining 2021: Workshop on Data Mining and Machine Learning for Cybersecurity - Online
Duration: 07 Dec 202110 Dec 2021
https://icdm2021.auckland.ac.nz
https://www.computer.org/csdl/proceedings/icdmw/2021/1AjSCovc0wM (Proceedings)

Conference

ConferenceIEEE International Conference on Data Mining 2021
Period07/12/2110/12/21
OtherThe IEEE International Conference on Data Mining (ICDM) has established itself as the world’s premier research conference in data mining. It provides an international forum for presentation of original research results, as well as exchange and dissemination of innovative and practical development experiences. The conference covers all aspects of data mining, including algorithms, software, systems, and applications. ICDM draws researchers, application developers, and practitioners from a wide range of data mining related areas such as big data, deep learning, pattern recognition, statistical and machine learning,databases, data warehousing, data visualization, knowledge-based systems, and high-performance computing. By promoting novel, high-quality research findings, and innovative solutions to challenging data mining problems, the conference seeks to advance the state-of-the-art in data mining.
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