Proceedings Article10.1109/ICIIECS.2017.8276047
Onto-based sentiment classification using machine learning techniques
K. Saranya,S. Jayanthy +1 more
- 17 Mar 2017
- pp 1-5
23
TL;DR: This paper is a review of all the machine learning techniques that can be applied on the semantic analysis of sentiments and proposes an onto-based process to analyse the customer's emotion.
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Abstract: Sentiment analysis is a methodology used to analyse the emotion or view of an individual to a situation or topic In present scenario, Social media is the source for the collection of individual's feedbacks, user's emotions, reviews and personal experiences which lead to a need for efficient mining of the text to derive knowledge An optimal classification of text based on emotion is an unsolved problem in text mining To extract knowledge from text many machine learning tools and techniques were proposed An onto-based process is proposed to analyse the customer's emotion in this paper The input emotional text that needs to be classified is given as input to the NLP and processed and an emotional ontology is created for better understanding of the semantics and relationships When adding new instances, Ontology can be automatically classify them based on emotional relationship The Emowords from ontology can be further classified using any of the standard machine learning techniques which definitively gives a better performance This paper is a review of all the machine learning techniques that can be applied on the semantic analysis of sentiments
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Citations
Sentiment analysis for e-commerce product reviews by deep learning model of Bert-BiGRU-Softmax
TL;DR: The proposed deep learning model of Bert-BiGRU-Softmax with hybrid masking, review extraction and attention mechanism, which applies sentiment Bert model as the input layer to extract multi-dimensional product feature from e-commerce reviews, outperforms the other deep learning models, including RNN, BiGRU, and Bert- biLSTM.
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Text emotion classification system based on multifractal methods
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Word synonym relationships for text analysis: A graph-based approach.
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A Literature Survey: Semantic Technology Approach in Machine Learning
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References
•Posted Content
Thumbs up? Sentiment Classification using Machine Learning Techniques
TL;DR: This article used machine learning techniques such as Naive Bayes, maximum entropy classification, and support vector machines (SVM) for sentiment classification of movie reviews, and found that SVM outperformed human-produced baselines.
Sentiment analysis algorithms and applications: A survey
TL;DR: This survey paper tackles a comprehensive overview of the last update in this field of sentiment analysis with sophisticated categorizations of a large number of recent articles and the illustration of the recent trend of research in the sentiment analysis and its related areas.
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Emotions from Text: Machine Learning for Text-based Emotion Prediction
Cecilia Ovesdotter Alm,Dan Roth,Richard Sproat +2 more
- 06 Oct 2005
TL;DR: This paper explores the text-based emotion prediction problem empirically, using supervised machine learning with the SNoW learning architecture to classify the emotional affinity of sentences in the narrative domain of children's fairy tales, for subsequent usage in appropriate expressive rendering of text-to-speech synthesis.
SENTIMENT CLASSIFICATION of MOVIE REVIEWS USING CONTEXTUAL VALENCE SHIFTERS
Alistair Kennedy,Diana Inkpen +1 more
- 01 May 2006
TL;DR: It is shown that extending the term‐counting method with contextual valence shifters improves the accuracy of the classification, and combining the two methods achieves better results than either method alone.
Ontology Matching: A Machine Learning Approach
AnHai Doan,Jayant Madhavan,Pedro Domingos,Alon Halevy +3 more
- 01 Jan 2004
TL;DR: This chapter studies ontology matching: the problem of finding the semantic mappings between two given ontologies, which lies at the heart of numerous information processing applications.