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Sentiment Analysis of Film Review Texts Based on Sentiment Dictionary and SVM
Proceedings of the 2019 3rd International Conference on Innovation in Artificial Intelligence, 2019The sentiment analysis of the film review text is to extract and analyze the hidden sentiment information in the text data, thereby helping the network personnel such as the media platform to analyze the audience's preference for the film. Based on this, this paper proposes a film review text sentiment analysis method based on SVM classification ...
Kui Lu, Jiesheng Wu
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Disclosure Sentiment: Machine Learning vs. Dictionary Methods
Management Science, 2022We compare the ability of dictionary-based and machine-learning methods to capture disclosure sentiment at 10-K filing and conference-call dates. Like Loughran and McDonald [Loughran T, McDonald B (2011) When is a liability not a liability? Textual analysis, dictionaries, and 10-Ks. J. Finance 66(1):35–65.], we use returns to assess sentiment. We find
Richard Frankel +2 more
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Sentiment Classification of Movie Reviews Using Korean Sentiment Dictionary
Advanced Science and Technology Letters, 2014While there exists a large volume of research on sentiment classification of English customer reviews using English sentiment dictionaries, there are few researches on classifying sentiment of Korean customer reviews using Korean sentiment dictionaries.
Heeryon Cho, Sang-Hyun Choi
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CSR & Sentiment Analysis: A New Customized Dictionary
2023Communication concerning the CSR pillars is key to sustainable corporate development. Sentiment analysis (SA) is a sub-area of natural language processing for studying communication through the classification of negative or positive opinions. Measuring sentiment is characterized by pitfalls related to: a) the context, where the polarity classification ...
Emma Zavarrone, Alessia Forciniti
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Text sentiment classification based on the automatic expansion of sentiment dictionary
Proceedings of the 5th International Conference on Communication and Information Processing, 2019The sentiment dictionary-based text sentiment classification method is one of the main methods in the field of sentiment analysis, and the completeness of the sentiment dictionary is one of the key factors of the method. With the constant appearance of the new terms on Internet, the static sentiment dictionary cannot cover all the sentiment words.
Haijie Pang, Hua Zhao, Chenjing Gong
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Text sentiment classification for SNS-based marketing using domain sentiment dictionary
2012 IEEE International Conference on Consumer Electronics (ICCE), 2012In this paper, we propose a new method of classifying the sentiment behind tweets that contains formal and informal vocabulary. Previous methods used only formal vocabulary to classify the sentiments behind the sentences. However, these methods are ineffective in classifying texts since internet users make sentences using informal vocabulary.
Sang-Hyun Cho, Hang-Bong Kang
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Polarity Consistency Checking for Sentiment Dictionaries.
2021Polarity classification of words is important for applications such as Opinion Mining and Sentiment Analysis. A number of sentiment word/sense dictionaries ave been manually or (semi)automatically constructed. The dictionaries have substantial inaccuracies.
Dragut, Eduard +4 more
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Sentiment Dictionary Refinement Using Word Embeddings
2015Previous works on Polish sentiment dictionaries revealed the superiority of machine learning on vectors created from word contexts (concordances or word co-occurrence distributions), especially compared to the SO-PMI method (semantic orientation of pointwise mutual information).
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Large Sentiment Dictionary of Russian Words
2023Vladimir V. Bochkarev +4 more
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Dictionary-based sentiment analysis with crowdcoding
2015Negativity is an important dimension in the social sciences: e.g. in the study of news values, negative campaigning or political polarization. Our paper presents a supervised procedure for fine-grained negative sentiment analysis at the sentence level. We develop a dictionary of negative valued political vocabulary and establish the dictionary’s terms’
Haselmayer, Martin, Jenny, Marcelo
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