Yıl: 2023 Cilt: 12 Sayı: 2 Sayfa Aralığı: 412 - 417 Metin Dili: İngilizce DOI: 10.17798/bitlisfen.1229868 İndeks Tarihi: 03-07-2023

Sentiment Analyzing from Tweet Data’s Using Bag of Words and Word2Vec

Öz:
Twitter sentiment classification is an artificial approach for examining textual information and figuring out what people's publicly tweets from a variety of industries are experiencing or thinking. For instance, a large number of tweets containing hashtags are posted online every minute from one user to some other user in the commercial and politics fields. It can be challenging for scientists to correctly comprehend the context in which specific tweet terms are used, necessitating a challenge in determining what is actually a positive or negative comment from the vast database of twitter data. The system's authenticity is violated by this issue and user dependability may be significantly diminished. In this study, twitter data sent to interpret movies were classified using various classifier and feature methods. In this context, the IMDB database consisting of 50000 movie reviews was used. For the purpose of anticipating the sentimental tweets for categorization, a huge proportion of twitter data is analyzed. In the proposed method, bag of words and word2vec methods are given by combining them instead of giving them separately to the classifier. With both the suggested technique, the system's effectiveness is increased and the data that are empirically obtained from the real world situation may be distinguished well. With experimental efficiency of 90%, the suggested approach algorithms' output attempts to assess the reviews tweets as well as be able to recognize movie reviews.
Anahtar Kelime: Natural language processing Bag of Words sentiment analysis Word2Vec

Belge Türü: Makale Makale Türü: Araştırma Makalesi Erişim Türü: Erişime Açık
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APA Aydın Y (2023). Sentiment Analyzing from Tweet Data’s Using Bag of Words and Word2Vec. , 412 - 417. 10.17798/bitlisfen.1229868
Chicago Aydın Yıldız Sentiment Analyzing from Tweet Data’s Using Bag of Words and Word2Vec. (2023): 412 - 417. 10.17798/bitlisfen.1229868
MLA Aydın Yıldız Sentiment Analyzing from Tweet Data’s Using Bag of Words and Word2Vec. , 2023, ss.412 - 417. 10.17798/bitlisfen.1229868
AMA Aydın Y Sentiment Analyzing from Tweet Data’s Using Bag of Words and Word2Vec. . 2023; 412 - 417. 10.17798/bitlisfen.1229868
Vancouver Aydın Y Sentiment Analyzing from Tweet Data’s Using Bag of Words and Word2Vec. . 2023; 412 - 417. 10.17798/bitlisfen.1229868
IEEE Aydın Y "Sentiment Analyzing from Tweet Data’s Using Bag of Words and Word2Vec." , ss.412 - 417, 2023. 10.17798/bitlisfen.1229868
ISNAD Aydın, Yıldız. "Sentiment Analyzing from Tweet Data’s Using Bag of Words and Word2Vec". (2023), 412-417. https://doi.org/10.17798/bitlisfen.1229868
APA Aydın Y (2023). Sentiment Analyzing from Tweet Data’s Using Bag of Words and Word2Vec. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, 12(2), 412 - 417. 10.17798/bitlisfen.1229868
Chicago Aydın Yıldız Sentiment Analyzing from Tweet Data’s Using Bag of Words and Word2Vec. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi 12, no.2 (2023): 412 - 417. 10.17798/bitlisfen.1229868
MLA Aydın Yıldız Sentiment Analyzing from Tweet Data’s Using Bag of Words and Word2Vec. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, vol.12, no.2, 2023, ss.412 - 417. 10.17798/bitlisfen.1229868
AMA Aydın Y Sentiment Analyzing from Tweet Data’s Using Bag of Words and Word2Vec. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi. 2023; 12(2): 412 - 417. 10.17798/bitlisfen.1229868
Vancouver Aydın Y Sentiment Analyzing from Tweet Data’s Using Bag of Words and Word2Vec. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi. 2023; 12(2): 412 - 417. 10.17798/bitlisfen.1229868
IEEE Aydın Y "Sentiment Analyzing from Tweet Data’s Using Bag of Words and Word2Vec." Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, 12, ss.412 - 417, 2023. 10.17798/bitlisfen.1229868
ISNAD Aydın, Yıldız. "Sentiment Analyzing from Tweet Data’s Using Bag of Words and Word2Vec". Bitlis Eren Üniversitesi Fen Bilimleri Dergisi 12/2 (2023), 412-417. https://doi.org/10.17798/bitlisfen.1229868