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Data preprocessing is an integral part of sentiment analysis, especially when it comes to unstructured data from Twitter. This paper describes a fairly comprehensive method of data preprocessing for Twitter data, so that the sentiment analysis can be performed effectively. The proposed method involves a number of steps, such as data preprocessing, tokenization, stemming, lemmatization, and feature engineering. Different machine learning algorithms like Naive Bayes, Support Vector Machines, Random Forest, and Long Short-Term Memory Networks are employed to evaluate the efficiency of the sentiment classification process. The experimental results show that proper preprocessing methods can improve the performance of sentiment analysis by up to 15% compared with models trained on raw data.
Keywords:
Twitter, Data preprocessing, Sentiment analysis, Machine Learning, Text mining, feature engineering.
Cite Article:
"DATA PREPROCESSING FOR SENTIMENT ANALYSIS USING TWITTER DATA", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.11, Issue 6, page no.a147-a158, July-2026, Available :http://www.ijrti.org/papers/IJRTI2606018.pdf
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ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator