Shah, ParitaParitaShahSwaminarayan, PriyaPriyaSwaminarayanPatel, MaitriMaitriPatel2025-08-312025-08-312022-02-0110.11591/ijece.v12i1.pp1030-10392-s2.0-85118929034https://d8.irins.org/handle/IITG2025/25140Opinion analysis is by a long shot most basic zone of characteristic language handling. It manages the portrayal of information to choose the motivation behind the wellspring of the content. The reason might be of a type of gratefulness (positive) or study (negative). This paper offers a correlation between the outcomes accomplished by applying the calculation arrangement using various classifiers for instance K-nearest neighbor and multinomial naive Bayes. These techniques are utilized to assess a significant assessment with either a positive remark or negative remark. The gathered information considered on the grounds of the extremity film datasets and an association with the results accessible proof has been created for a careful assessment. This paper investigates the word level count vectorizer and term frequency inverse document frequency (TF-IDF) influence on film sentiment analysis. We concluded that multinomial naive Bayes (MNB) classier generate more accurate result using TF-IDF vectorizer compared to CountVectorizer, K-nearest-neighbors (KNN) classifier has the same accuracy result in case of TF-IDF and CountVectorizer.trueClassifier | Features selection | Film | Gujarati | Precision | SentimentalityReviewhttps://ijece.iaescore.com/index.php/IJECE/article/download/24642/154411030-1039February 202226reJournal13