Opinion mining using ensemble text hidden Markov models for text classification

Kang, Mangi; Ahn, Jaelim; Lee, Kichun

doi:10.1016/j.eswa.2017.07.019

Cited by 135 publications

(75 citation statements)

References 7 publications

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“…Many approaches have been deployed for performing it. Traditionally, the approaches that can be found in literature for text classification include naive Bayes classifier, k-nearest neighbors, artificial neural network, evolutionary approaches, support vector machines, decision trees etc [8]- [11]. The training of the classifier can be either feature based or end-to-end learning without the need of the step of feature extraction.…”

Section: Literature Surveymentioning

confidence: 99%

Text Classification Using Ensemble Of Non-Linear Support Vector Machines

Sharma¹,

Sharma²

2019

IJITEE

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With the advent of digital era, billions of the documents generate every day that need to be managed, processed and classified. Enormous size of text data is available on world wide web and other sources. As a first step of managing this mammoth data is the classification of available documents in right categories. Supervised machine learning approaches try to solve the problem of document classification but working on large data sets of heterogeneous classes is a big challenge. Automatic tagging and classification of the text document is a useful task due to its many potential applications such as classifying emails into spam or non-spam categories, news articles into political, entertainment, stock market, sports news, etc. The paper proposes a novel approach for classifying the text into known classes using an ensemble of refined Support Vector Machines. The advantage of proposed technique is that it can considerably reduce the size of the training data by adopting dimensionality reduction as pre-training step. The proposed technique has been used on three bench-marked data sets namely CMU Dataset, 20 Newsgroups Dataset, and Classic Dataset. Experimental results show that proposed approach is more accurate and efficient as compared to other state-of-the-art methods.

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Section: Literature Surveymentioning

confidence: 99%

Text Classification Using Ensemble Of Non-Linear Support Vector Machines

Sharma¹,

Sharma²

2019

IJITEE

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“…Opinion mining gather required emotional words defined in advance and also have some complexity to classify the sentences that involve a judgment without the use of any emotional keywords. In [12] the researcher proposes a novel emotion analysis technique, based on hidden Markov design with text data. In this research, there is a text-based representation of emotions via ensemble TextHMMs.…”

Section: Related Workmentioning

confidence: 99%

Text Clustering using Ensemble Clustering Technique

Mateen¹,

Wen²,

Hassan³

et al. 2018

ijacsa

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Clustering is being used in different fields of research, including data mining, taxonomy, document retrieval, image segmentation, pattern classification. Text clustering is a technique through which text/ documents are divided into a particular number of groups, so that text within each group is related in contents. In this paper, the idea of ensemble text clustering of majority voting is defined. For this purpose, different clustering methods such as fuzzy c-means, k-means, agglomerative, Gustafson Kessel and k-medoid are used. After performing the pre-processing of the documents, inverse document frequency (IDF) has been achieved by the provided dataset. The achieved IDF is considered as input to the clustering algorithms. Dunn Index and Davies Bouldin Index have been calculated which are applied to analyze the usefulness of the proposed ensemble clustering. In this work, a dataset "Textclus" which contains four different classes, history, education, politician and art as a text is applied. Additionally, another dataset "20newsgroups" is also applied for analysis. The clustering quality measures have also been calculated from the proposed ensemble clustering results. The attained results show that the proposed ensemble clustering outperforms the other state of the art clustering techniques.

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“…The sentiment analysis, also known as opinion mining, is a study to determine public opinion about services, products, films, and so on. The need for sentiment analysis has become very important [7]. When people will make decisions, they will refer to the opinions of others.…”

Section: A Sentiment Analysismentioning

confidence: 99%

“…When people will make decisions, they will refer to the opinions of others. Almost all companies conduct market surveys and research to capture public opinion about their products, as well as in government, to see how government performance can be done by studying public opinion [7]. The need for sentiment analysis is growing substantially, especially with the development of social media as a place for society to express its opinions.…”

Section: A Sentiment Analysismentioning

confidence: 99%

Sentiment Analysis Based on Appraisal Theory for Assessing Incumbent Electability

Canrakerta¹,

Putro²,

Irfandi³

et al. 2018

EECSI

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Sentiment analysis is a useful study for determining opinions by classifying text. The document used in the research comes from Twitter about public opinion about community satisfaction related to performance of incumbent. The method used is Appraisal Theory. The data used are 1587 for Jokowi related data, 1774 for Ministry related data, and 1337 government related data. The result of data analysis from this research is that people have positive sentiments for incumbent. Innovation is a term that has the highest positive sentiment, whereas imaging is the term that has the lowest negative sentiment.

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Opinion mining using ensemble text hidden Markov models for text classification

Cited by 135 publications

References 7 publications

Text Classification Using Ensemble Of Non-Linear Support Vector Machines

Text Classification Using Ensemble Of Non-Linear Support Vector Machines

Text Clustering using Ensemble Clustering Technique

Sentiment Analysis Based on Appraisal Theory for Assessing Incumbent Electability

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