Application of machine learning techniques to sentiment analysis

Jain, Anuja P; Dandannavar, Padma

doi:10.1109/icatcct.2016.7912076

Cited by 100 publications

(44 citation statements)

References 7 publications

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“…Some used Naïve Bayes, random forest, and support vector machine to analysis sentiment of Facebook comments [5]. On the other hand, researcher [6] provides an indication of using Naïve Bayes along with decision tree for classification of sentiment from Facebook data. In this work, the authors proposed a machine learning framework based on Apache for dimensionality reduction since it focused on Big Data manipulation.…”

Section: Literature Reviewmentioning

confidence: 99%

Adam Deep Learning With SOM for Human Sentiment Classification

Ali

Sarowar

Rahman

et al. 2019

International Journal of Ambient Computing and Intelligence

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Nowadays, with the improvement in communication through social network services, a massive amount of data is being generated from user's perceptions, emotions, posts, comments, reactions, etc., and extracting significant information from those massive data, like sentiment, has become one of the complex and convoluted tasks. On other hand, traditional Natural Language Processing (NLP) approaches are less feasible to be applied and therefore, this research work proposes an approach by integrating unsupervised machine learning (Self-Organizing Map), dimensionality reduction (Principal Component Analysis) and computational classification (Adam Deep Learning) to overcome the problem. Moreover, for further clarification, a comparative study between various well known approaches and the proposed approach was conducted. The proposed approach was also used in different sizes of social network data sets to verify its superior efficient and feasibility, mainly in the case of Big Data. Overall, the experiments and their analysis suggest that the proposed approach is very promissing.

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Section: Literature Reviewmentioning

confidence: 99%

Adam Deep Learning With SOM for Human Sentiment Classification

Ali

Sarowar

Rahman

et al. 2019

International Journal of Ambient Computing and Intelligence

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“…The goal is to classify unknown and unlabeled data using the information from the labelled data set. c. Hybrid approach: Hybrid or blended techniques includes mixture of both machine learning algorithms and lexicon based techniques for classification [7]. The principal advantage offered by hybrid techniques is that we can achieve combined accuracy of both lexicon techniques and machine learning based classification.…”

Section: Semi-supervised Learningmentioning

confidence: 99%

Machine Learning Techniques for Sentiment Analysis of Indian Languages

2019

IJRTE

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Sentiment Analysis is the domain of automatically understanding the emotions, feelings, opinions in a textual data. It is a way of understating how a product, brand, service, idea or an event is viewed by common people, customers and stakeholders. Sentiment Analysis Systems are used by politicians, business leaders, developers and researchers to infer useful information as per their specific needs. It is used in business decision making process to value the views of the customers. Sentiment analysis has become a hot topic of scientific and market research in the field of natural Language Processing. India is a large populated country and the number of Internet users is also huge. Most people share their experience in English. However, during the last decade, due to the accessibility of Internet and evolution in language modelling people express their views in their own native Indian language. With the increase in Indian language text, researchers find it quite fascinating to infer valuable information from this unstructured text data. A number of machine learning techniques have been applied on this textual data set. Basic concepts of Sentiment analysis shall be discussed with focus on Indian language text in this paper. Due to on availability of rich lexicon resources for unsupervised learning techniques and better evaluation measures for the Supervised learning techniques, the later become the first choice for researchers in the field of Natural Language Processing. A comparative analysis shall be made for various supervised machine learning techniques in the context of Indian languages.

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“…T. H. A. Soliman et al [38] have carried out mining of online customer reviews utilizing support vector machines and a similar work on sentiment analysis has been reported in [39] based on As-LDA model. There is an interesting work reported on sentiment analysis based on machine learning techniques in [40]. Sentence level sentiment analysis has been carried out using cloud machine learning techniques in [41].…”

Section: Introductionmentioning

confidence: 99%

Sentiment Analysis, Visualization and Classification of Summarized News Articles: A Novel Approach

Urologin¹

2018

ijacsa

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Due to advancement in technology, enormous amount of data is generated every day. One of the main challenges of large amount of data is user overloaded with huge volume of data. Hence effective methods are highly required to help user to comprehend large amount of data. This research work proposes effective methods to extract and represent the data. The summarization is applicable to obtain a brief overview of the text and sentiment analysis can obtain emotions expressed in the text computationally. The combined text summarization and sentiment analysis is proposed on BBC news articles. A pronoun replacement based text summarization method is developed and VADER sentiment analyzer is used to determine sentiment information. The 3-D visualization schemes have been provided to represent the sentiment information. The sentiment analysis and classification are performed on original BBC news articles as well as on summarized articles using classifiers, such as Logistic Regression, Random Forest and Adaboost. On original news articles highest classification rate of 84.93%, using summarization of ratio 25%, 50% and 75% highest classification rates of 78.73%, 83.06% and 83.23%, respectively are observed.

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Application of machine learning techniques to sentiment analysis

Cited by 100 publications

References 7 publications

Adam Deep Learning With SOM for Human Sentiment Classification

Adam Deep Learning With SOM for Human Sentiment Classification

Machine Learning Techniques for Sentiment Analysis of Indian Languages

Sentiment Analysis, Visualization and Classification of Summarized News Articles: A Novel Approach

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