Sentiment Analysis on Movie Reviews: A Comparative Study of Machine Learning Algorithms and Open Source Technologies

Narendra, Bhogireddy; Sai, K. Uday; Rajesh, G; Kolla, Hemanth; Teja, M. V. Chaitanya; Kumar, Kiran

doi:10.5815/ijisa.2016.08.08

Cited by 21 publications

(9 citation statements)

References 13 publications

(8 reference statements)

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“…Twitter-based opinion mining platform was developed by [4]. The variables used for data collection are important keywords [16,17]. The sentiment value is addition of weights which is extracted from text.…”

Section: Related Workmentioning

confidence: 99%

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Prediction of Election by Twitter

Kusum¹,

Panda²

2020

IJEAT

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Nowadays social media like Twitter and Facebook etc. is one of the key players. Twitters are micro blogging sites by which users sent their opinions and views in brief. The information generated by one user can be seen by everyone.Therefore to analyze twitter sentiment can be a crucial task. For this task, we have used various approaches like novel based approach and machine learning and many other rules like context awareness are used for the detection of public opinion and prediction of results. We are studying the user tweets during elections . Meaningful tweets are collected on a definite period.The feasibility of the developed classification model is identified by our proposed work to identify the political orientation on the tweets and other user-based features. The technique for the collection of tweets in time has played an important role. When the outcome of applied technique competes with survey agencies result was published before elections result.

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Section: Related Workmentioning

confidence: 99%

“…Important variables of data collection are keywords that help in meaningful tweets [16] [17]. Many kinds of research of keyword collections are used: Candidate's name, Party's name, Election-related features, and campaign hashtags; Time bound to elections which is about 2 months.…”

Section: Proposed Workmentioning

confidence: 99%

Prediction of Election by Twitter

Kusum¹,

Panda²

2020

IJEAT

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“…The important variable for data collection from social media data are the keywords which help in getting relevant tweets [57,58]. Most research for keyword selection used candidate/party names, electionrelated hashtags, and campaign hashtags [18].The period for the conduction of election takes almost 2 to 6 months.…”

Section: Proposed Data Collection Methodologymentioning

confidence: 99%

Towards Prediction of Election Outcomes Using Social Media

Jain¹,

Kumar²

2017

IJISA

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Abstract-Exploiting social media data by extracting key information from it is one of the great challenges in data mining and knowledge discovery. Every election campaign has an online presence of voters which uses these social media platform to express their sentiments and opinions towards political parties, leaders and important topics. This paper presents a novel data collection technique for prediction of election outcomes and a topic modeling method for extracting topics. Data collection technique used RSS (Rich Site Summary) feeds of news articles and trending keywords from Twitter simultaneously and constructed an intelligent prediction model based primarily on the volume of tweets and sentiment of users. This paper effort to improve electoral predictions using social media data based dynamic keyword methodology.Different techniques for electoral prediction based on social media data has been investigated based on existing literature and isolate the factors which improve our methodology. Meaningful inferences such as the popularity of leaders and parties during different intervals, trending issues, and important factors are extracted from the data set. The election outcomes are compared with traditional methods used by survey agencies for exit polls and validation of results showed that social media data can predict with better accuracy. The research has identified that data collection technique and timing play an important role in yielding better accuracy in predicting outcomes and extracting meaningful inferences.

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“…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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Sentiment Analysis on Movie Reviews: A Comparative Study of Machine Learning Algorithms and Open Source Technologies

Cited by 21 publications

References 13 publications

Prediction of Election by Twitter

Prediction of Election by Twitter

Towards Prediction of Election Outcomes Using Social Media

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

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