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A Machine Learning-Based Technique with Intelligent WordNet Lemmatize for Twitter Sentiment Analysis

S. Saranya*, G. Usha

Computing Technology, SRMIST, Chennai, Tamil Nadu, India

* Corresponding Author: S. Saranya. Email: email

Intelligent Automation & Soft Computing 2023, 36(1), 339-352.


Laterally with the birth of the Internet, the fast growth of mobile strategies has democratised content production owing to the widespread usage of social media, resulting in a detonation of short informal writings. Twitter is microblogging short text and social networking services, with posted millions of quick messages. Twitter analysis addresses the topic of interpreting users’ tweets in terms of ideas, interests, and views in a range of settings and fields. This type of study can be useful for a variation of academics and applications that need knowing people’s perspectives on a given topic or event. Although sentiment examination of these texts is useful for a variety of reasons, it is typically seen as a difficult undertaking due to the fact that these messages are frequently short, informal, loud, and rich in linguistic ambiguities such as polysemy. Furthermore, most contemporary sentiment analysis algorithms are based on clean data. In this paper, we offers a machine-learning-based sentiment analysis method that extracts features from Term Frequency and Inverse Document Frequency (TF-IDF) and needs to apply deep intelligent wordnet lemmatize to improve the excellence of tweets by removing noise. We also utilise the Random Forest network to detect the emotion of a tweet. To authenticate the proposed approach performance, we conduct extensive tests on publically accessible datasets, and the findings reveal that the suggested technique significantly outperforms sentiment classification in multi-class emotion text data.


Cite This Article

S. Saranya and G. Usha, "A machine learning-based technique with intelligent wordnet lemmatize for twitter sentiment analysis," Intelligent Automation & Soft Computing, vol. 36, no.1, pp. 339–352, 2023.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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