TY - JOUR ID - 147360 TI - Machine Learning Approach to Sentiment Analysis in Data Mining JO - Passer Journal of Basic and Applied Sciences JA - PSR LA - en SN - 27065944 AU - Hussein, Dalshad Jaafar AU - Rashad, Mstafa Nihad AU - Mirza, Kosar Ibrahim AU - hussein, dana LATEEF AD - Database Department, Computer Science Institute, Sulaimani Polytechnic University, Kurdistan Region – Iraq AD - Information Technology Department, Chamchamal Technical Institute, Sulaimani Polytechnic University, Kurdistan Region – Iraq AD - Information Technology Department, Computer Science Institute, Sulaimani Polytechnic University Y1 - 2022 PY - 2022 VL - 4 IS - 1 SP - 71 EP - 77 KW - Machine Learning KW - Twitter KW - Sentiment analysis KW - Opinion mining KW - Social Medi DO - 10.24271/psr.2022.312664.1101 N2 - Widespread internet use and the web have brought about new ways of expressing individual sentiments. A sentiment is defined as an individual's view in which feelings, attitudes, and thoughts can be represented. When it comes to analysing and extracting Sentiment analysis and opinion mining are two of the most prominent disciplines of research. They derive insights using text data through numerous sources like Facebook and Twitter. Sentiment analysis frequently elicits information on how people feel about various events, brands, products, or businesses. Researchers collect and improvise replies from the general public to conduct evaluations. This paper looks into sentiment analysis for classifying Twitter subscriber tweets. This approach can help analysing the information gathered and stored in positive, neutral and negative opinions. This information is first pre-processed before creating feature vectors. On the basis of machine learning, classification methods were used. The study's algorithms are used Maximum Entropy, Naive Bayes and Support Vector Machine; they are used to categorize documents as positive or negative. The dataset for this paper are obtained from Twitter and includes subscribed tweets by using the API. Following pre-processing, machine learning methods are used to determine whether the tweets are positive or negative. UR - https://passer.garmian.edu.krd/article_147360.html L1 - https://passer.garmian.edu.krd/article_147360_0dd16a0a38cc4259194e15758ad90e4c.pdf ER -