Document-level sentiment classification using hybrid machine learning approach

Tripathy, Abinash; Anand, Abhishek; Rath, Santanu Kumar

doi:10.1007/s10115-017-1055-z

Cited by 99 publications

(60 citation statements)

References 33 publications

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“…by many researchers achieving comparable results with the monolingual ones [13][14][15][16][17]. AbinashTripathy et al [18] and Yuhui Cao et al [19] mentioned that the combination of two different machine learning algorithms like SVM and ANN for sentiment classification yield better results when compared with other hybrid models. Yassine Al Amrani et al [20] chose to used SVM and Random Forest for sentiment classification and introduced a novel hybrid approach to identify product reviews obtained by Amazon.…”

Section: Related Workmentioning

confidence: 99%

Sentiment classification of social media reviews using an ensemble classifier

Sangam

Shinde

2019

IJEECS

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<p>These days it has become a common practice for business organizations and individuals to make use of social media for sharing the opinions about the products or the services. Consumers are also ready to share their views on certain products or commodities. Thus huge amount of unstructured social media data gets generated day by day. Gradually heap of text data will be formed in many areas like automated business, education, health care, and show business and so on. Opinion mining also referred as sentiment analysis or sentiment classification, deals with mining of the review text and classifying the opinions or the sentiments of that text as positive or negative. In this paper we propose an ensemble classifier model consisting of Support Vector Machine and Artificial Neural Network. It combines the knowledge from two feature sets for sentiment classification. The proposed model shows the acceptable performance in terms of accuracy when compared with the baseline model.</p>

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Section: Related Workmentioning

confidence: 99%

Sentiment classification of social media reviews using an ensemble classifier

Sangam

Shinde

2019

IJEECS

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“…Tripathy et al (2017) [23] conducted a study on sentiment analysis of movie reviews from IMDb and Polarity dataset. A hybrid system of SVM and ANN has been used for higher accuracy of results in which SVM did the job of selecting features, which are then used as input to ANN.…”

Section: Literature Reviewmentioning

confidence: 99%

“…On the Internet, the reviews by the customers and buyers play a major role in assessing the quality of a product or service. Several research papers have been published on prediction of box office success of movies [6][7][8][9][20][21][22][23][24][25][26][27][28][29]. In this paper, the author presents a 2 layered back-propagation neural network model with 23 numeric inputs (BPNN-N23) developed for prediction of the success status among 3 success classes.…”

Section: Introductionmentioning

confidence: 99%

Classification and Forecasting of Bollywood Movies by Commercial Success using Back-Propagation Neural Network model

Nayak*

2019

IJITEE

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Forecasting commercial success of motion pictures remained challenging for producers, critics and other industry leaders in this changing world of web and online media. In this study, the author has explored a back-propagation neural network model with 23 numeric input (BPNN-N23) for classification of Bollywood movies released during the years 2014 through 2017. The proposed model classifies movies in three classes namely “HIT”, “AVERAGE” and “FLOP”. Common procedures like data filtering, data cleaning and data normalization have been followed prior to feeding those data to the neural network. After comparing the performance of the proposed model with the benchmark models and works, the results show that the said model shows performance that is comparable to the published ones with respect to the assumed Indian empirical settings. This research reveals the extent of the effects and roles of the considered factors as well as the proposed model in predicting the fate of a Bollywood movie in India.

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“…Two of the Supervised machine learning classifiers in WEKA were used in this study -Support Vector Machine (SVM) and Multinomial Naïve-Bayes (MNB) classifiers. The choice to use these classifiers is rooted in the fact that considerable studies have found these classifiers efficient and robust for text classification [51,52]. In WEKA package, we employed 10-folds cross validation for training and predicting of the emotions in the text.…”

Section: Machine Predictionmentioning

confidence: 99%

Emotional Impact of Suicide on Active Witnesses: Predicting with Machine Learning

Agjei¹,

Kolog²,

Dei³

et al. 2018

Adv. sci. technol. eng. syst. j.

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Predicting the impact of suicide on incidental witnesses at an early stage helps to avert the possible side effect. When suicide is committed in public, incidental observers are left to grapple with it. In many cases, these incidental witnesses tend to experience the emotional side effect with time. In this study, we employed a Machine learning algorithms to predict the impact of suicide and suicidal attempt on incidental witnesses. This prediction was based on the accounts of suicide given by selected participants who have witnessed the act. The accounts, which was pre-processed into a corpus, were manually annotated with predefined emotion categories. While sadness emerged as the most salient emotional impact on the witnesses, fear was found as the lowest of the emotional impact on the witnesses. However, the machine learning prediction yielded highest in predicting depression with insignificant variations in the other emotional categories. This nonetheless shows that people who have witnessed suicide or suicidal attempts are inherently affected by some form of emotions that may require urgent attention to alleviate. By evaluating the performance of the Machine learning algorithms, the Support Vector Machine was superior, in terms its prediction, then the Multinomial Naïve Bayes algorithm. The outcome of the study contributes to the pool of research that sought to advocate the use of Machine Learning for predicting social phenomenon.

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Document-level sentiment classification using hybrid machine learning approach

Cited by 99 publications

References 33 publications

Sentiment classification of social media reviews using an ensemble classifier

Sentiment classification of social media reviews using an ensemble classifier

Classification and Forecasting of Bollywood Movies by Commercial Success using Back-Propagation Neural Network model

Emotional Impact of Suicide on Active Witnesses: Predicting with Machine Learning

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