Investigating the Effect of eWOM in Movie Box Office Success Through an Aspect-Based Approach

2018 ◽  
Vol 5 (1) ◽  
pp. 1-15 ◽  
Author(s):  
Saurav Mohanty ◽  
Nicolle Clements ◽  
Vipul Gupta
Keyword(s):  
Sentiment Analysis ◽  
Word Of Mouth ◽  
Strong Dependence ◽  
Domestic Market ◽  
User Reviews ◽  
Box Office ◽  
Sentiment Score ◽  
Negative Sentiment ◽  
Movie Success ◽  
The U.S

This study examines the influence of Electronic Word of Mouth (eWOM) on the box office revenue generation of movies in the U.S domestic market using the technique of Aspect-Based Sentiment Analysis (ABSA) and aspect identification. The analysis was conducted on the sentiment score and frequency of five movie aspects from the user reviews collected from high grossing 2014 movies. This study revealed a significant dependence on the aspect-based sentiment frequency of the movie's Story aspect. Surprisingly, the data also showed a strong dependence of movie success on the negative sentiment frequency on the Casting aspect. The findings of the study suggest that the eWOM present in online movie reviews can be used to predict the performance of a movie at the box office by monitoring the aspect's frequency of sentiment, which can be referred to as a metric of the online “buzz” of the movie.

scholarly journals Sentiment Analysis of Hotel User Review using RNN Algorithm

2021 ◽  
Vol 3 (1) ◽  
pp. 30
Author(s):  
Theresia Arwila Utami
Keyword(s):  
Sentiment Analysis ◽  
Monitoring And Evaluation ◽  
Research Area ◽  
Current Time ◽  
User Reviews ◽  
Textual Data ◽  
Long Time ◽  
Negative Sentiment ◽  
User Review

Sentiment analysis in user review is a growing research area at the current time. Usually, the website becomes a source of data in knowing the quality of the hotel services, and the provider can utilize the review for monitoring and evaluation. However, determining the positive or negative sentiment of a user review in unstructured textual data takes a long time. As a result, we present a model to classify positive or negative sentiment in user reviews in this article. This study suggests the RNN method in building an effective model to classify user sentiment. Based on the experiment, our model can produce accurate results in organizing hotel reviews. Furthermore, the proposed method achieved a higher evaluation metrics score with an f1-score of 91.0%.

Using Twitter data to predict the performance of Bollywood movies

2015 ◽  
Vol 115 (9) ◽  
pp. 1604-1621 ◽  
Author(s):  
Dipak Damodar Gaikar ◽  
Bijith Marakarkandy ◽  
Chandan Dasgupta
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Classification Model ◽  
Data Mining Algorithm ◽  
Content Type ◽  
Box Office ◽  
Twitter Data ◽  
Interface System ◽  
Sentiment Score ◽  
Movie Performance

Purpose – The purpose of this paper is to address the shortcomings of limited research in forecasting the power of social media in India. Design/methodology/approach – This paper uses sentiment analysis and prediction algorithms to analyze the performance of Indian movies based on data obtained from social media sites. The authors used Twitter4j Java API for extracting the tweets through authenticating connection with Twitter web sites and stored the extracted data in MySQL database and used the data for sentiment analysis. To perform sentiment analysis of Twitter data, the Probabilistic Latent Semantic Analysis classification model is used to find the sentiment score in the form of positive, negative and neutral. The data mining algorithm Fuzzy Inference System is used to implement sentiment analysis and predict movie performance that is classified into three categories: hit, flop and average. Findings – In this study the authors found results of movie performance at the box office, which had been based on fuzzy interface system algorithm for prediction. The fuzzy interface system contains two factors, namely, sentiment score and actor rating to get the accurate result. By calculation of opening weekend collection, the authors found that that the predicted values were approximately same as the actual values. For the movie Singham Returns over method of prediction gave a box office collection as 84 crores and the actual collection turned out to be 88 crores. Research limitations/implications – The current study suffers from the limitation of not having enough computing resources to crawl the data. For predicting box office collection, there is no correct availability of ticket price information, total number of seats per screen and total number of shows per day on all screens. In the future work the authors can add several other inputs like budget of movie, Central Board of Film Certification rating, movie genre, target audience that will improve the accuracy and quality of the prediction. Originality/value – The authors used different factors for predicting box office movie performance which had not been used in previous literature. This work is valuable for promoting of product and services of the firms.

scholarly journals Sentiment Analysis on Tokopedia Product Online Reviews Using Random Forest Method

2020 ◽  
Vol 202 ◽  
pp. 16006
Author(s):  
Stephenie ◽  
Budi Warsito ◽  
Alan Prahutama
Keyword(s):  
Random Forest ◽  
Sentiment Analysis ◽  
Online Reviews ◽  
Consumer Products ◽  
Classification Model ◽  
Product Reviews ◽  
Validation Data ◽  
Random Forest Method ◽  
Sentiment Score ◽  
Negative Sentiment

Tokopedia is one of the most popular e-commerce sites in Indonesia that offers consumer products from various categories. In each product section, a review feature is offered. This review feature became essential in evaluating the sellers and become one consideration for customers in making purchase consideration. Sentiment analysis of Tokopedia product reviews may provide the opportunity to look on how Tokopedia customers respond to product quality and sellers’ hospitality. In evaluating the model, the reviews were grouped as: “positive sentiment” and “negative sentiment” using the Random Forest method and 10-fold cross-validation. Data labelling was carried out automatically by calculating the sentiment score using Lexicon-Based. Visualization of the labelling results was then done using a bar graph and a word cloud on each class of sentiment in order to look up for information that is considered important and most discussed. The test results showed that the accuracy of the Random Forest Method with parameter mtry = 73 and ntree = 50 is 97.38% which leads to the conclusion that the Random Forest Method could well predict the product reviews of Tokopedia. The greater the accuracy, the better performance of the classification model.

Document Preprocessing with TF-IDF to Improve the Polarity Classification Performance of Unstructured Sentiment Analysis

2020 ◽  
pp. 235-242
Author(s):  
Farrikh Alzami ◽  
Erika Devi Udayanti ◽  
Dwi Puji Prabowo ◽  
Rama Aria Megantara
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Random Forest ◽  
Sentiment Analysis ◽  
Classification Performance ◽  
Document Preparation ◽  
Learning Models ◽  
Polarity Classification ◽  
Negative Sentiment ◽  
Machine Learning Models

Sentiment analysis in terms of polarity classification is very important in everyday life, with the existence of polarity, many people can find out whether the respected document has positive or negative sentiment so that it can help in choosing and making decisions. Sentiment analysis usually done manually. Therefore, an automatic sentiment analysis classification process is needed. However, it is rare to find studies that discuss extraction features and which learning models are suitable for unstructured sentiment analysis types with the Amazon food review case. This research explores some extraction features such as Word Bags, TF-IDF, Word2Vector, as well as a combination of TF-IDF and Word2Vector with several machine learning models such as Random Forest, SVM, KNN and Naïve Bayes to find out a combination of feature extraction and learning models that can help add variety to the analysis of polarity sentiments. By assisting with document preparation such as html tags and punctuation and special characters, using snowball stemming, TF-IDF results obtained with SVM are suitable for obtaining a polarity classification in unstructured sentiment analysis for the case of Amazon food review with a performance result of 87,3 percent.

Assessment of Agreement Between Human Ratings and Lexicon-Based Sentiment Ratings of Open-Ended Responses on a Behavioral Rating Scale

Assessment ◽  
2021 ◽  
pp. 107319112199646
Author(s):  
Olivia Gratz ◽  
Duncan Vos ◽  
Megan Burke ◽  
Neelkamal Soares
Keyword(s):  
Sentiment Analysis ◽  
Language Processing ◽  
Rating Scales ◽  
Clinical Decision Making ◽  
Rating Scale ◽  
Clinical Decision ◽  
Assessment System ◽  
Word Count ◽  
Positive Correlation ◽  
Sentiment Score

To date, there is a paucity of research conducting natural language processing (NLP) on the open-ended responses of behavior rating scales. Using three NLP lexicons for sentiment analysis of the open-ended responses of the Behavior Assessment System for Children-Third Edition, the researchers discovered a moderately positive correlation between the human composite rating and the sentiment score using each of the lexicons for strengths comments and a slightly positive correlation for the concerns comments made by guardians and teachers. In addition, the researchers found that as the word count increased for open-ended responses regarding the child’s strengths, there was a greater positive sentiment rating. Conversely, as word count increased for open-ended responses regarding child concerns, the human raters scored comments more negatively. The authors offer a proof-of-concept to use NLP-based sentiment analysis of open-ended comments to complement other data for clinical decision making.

scholarly journals Enhanced concept-level sentiment analysis system with expanded ontological relations for efficient classification of user reviews

2021 ◽  
Author(s):  
Asad Khattak ◽  
Muhammad Zubair Asghar ◽  
Zain Ishaq ◽  
Waqas Haider Bangyal ◽  
Ibrahim A Hameed
Keyword(s):  
Sentiment Analysis ◽  
User Reviews ◽  
Analysis System

The Significance of Government's Role in Technology Standardization

2010 ◽  
Vol 8 (1) ◽  
pp. 63-74 ◽  
Author(s):  
DongBack Seo
Keyword(s):  
Wireless Communications ◽  
First Generation ◽  
Rapid Development ◽  
Global Market ◽  
Domestic Market ◽  
Economic Consequences ◽  
Technology Market ◽  
Early Decision ◽  
Korean Market ◽  
The U.S

For first generation (1G) wireless communications technology standards, the Japanese government’s early decision provided an opportunity for its national manufacturers to be first movers in the global market, while the late development of wireless communications in Korea made the Korean market dependent on foreign manufacturers by adopting the U.S. standard (AMPS). Moving toward the 2G wireless technology market, both countries decided to develop standards instead of adopting a technology from outside their regions. Japan developed its own standard, PDC, while Korea developed CDMA systems with Qualcomm, the U.S. technology provider. Although these governments’ decisions on technologies looked only slightly different, the socio-economic consequences were greatly distinctive. The Korean success brought not only the rapid development of its domestic market but also opportunities for its manufacturers to become global leaders, while the PDC standard only provided the fast growth of the Japanese domestic market without any opportunities for the Japanese manufacturers to grow further internationally in the 1990s. By the end of 1990s, two nations again had to decide a 3G technology standard with vast challenges and pressures.

scholarly journals Social listening – revealing Parkinson’s disease over day and night

BMC Neurology ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Hui Zhang ◽  
Fanwen Meng ◽  
Xingyu Li ◽  
Yali Ning ◽  
Meng Cai
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Unmet Needs ◽  
Unmet Need ◽  
Motor Symptoms ◽  
Motor Complications ◽  
Sentiment Score ◽  
Negative Sentiment ◽  
Non Motor Symptoms ◽  
Social Listening

Abstract Background Nocturnal symptoms in Parkinson’s disease are often treated after management of daytime manifestations. In order to better understand the unmet needs of nocturnal symptoms management, we analyzed the characteristics and burden of nocturnal symptoms from patients’ perspectives and explored their changes over time. Overall symptoms (occurring at day or night) were collected to compare whether the unmet needs related to nocturnal symptoms and to overall symptoms are different. Methods We used a Social Listening big-data technique to analyze large amounts of Parkinson’s disease symptoms in dialogues available from social media platforms in 2016 to 2018. These symptoms were classified as either overall symptoms or nocturnal symptoms. We used share of voice (SOV) of symptoms as a proportion of total dialogues per year to reflect the characteristics of symptoms. Negative sentiment score of symptoms was analyzed to find out their related burden. Results We found the SOV for overall motor symptoms was 79% and had not increased between 2016 and 2018 (79%, p = 0.5). The SOV for non-motor symptoms was 69% and had grown by 7% in 2018 (p <  0.01). The SOV for motor complications was 9% and had increased by 6% in 2018 (p <  0.01). The SOV of motor symptoms was larger than non-motor symptoms and motor complications (p <  0.01). The SOV of non-motor symptoms was larger than motor complications (p <  0.01). For nocturnal symptoms, 45% of the analyzed PD population reported nocturnal symptoms in 2018, growing by 6% (p <  0.01). The SOV for nocturnal-occurring motor symptoms was higher than most non-motor symptoms. However, non-motor symptoms had the higher increases and evoked higher negative sentiment regardless of whether they occurred during the day or night. For symptoms that can occur at either day or night, each nocturnal symptom was rated with a higher negative sentiment score than the same symptom during the day. Conclusions The growing SOV and the greater negative sentiment of nocturnal symptoms suggest management of nocturnal symptoms is an unmet need of patients. A greater emphasis on detecting and treating nocturnal symptoms with 24-h care is encouraged.

Using Sentiment Analysis to Understand What Patients Are Saying About Hand Surgeons Online

Hand ◽  
2021 ◽  
pp. 155894472110604
Author(s):  
Justin E. Tang ◽  
Varun Arvind ◽  
Christopher A. White ◽  
Calista Dominy ◽  
Jun S. Kim ◽  
...  
Keyword(s):  
Sentiment Analysis ◽  
Demographic Variables ◽  
Physician Review ◽  
Bedside Manner ◽  
Relieve Pain ◽  
Significant Difference ◽  
Expectation Management ◽  
Analysis Package ◽  
Sentiment Score ◽  
The Relationship

Background: Physician review websites have influence on a patient’s selection of a provider. Written reviews are subjective and difficult to quantitatively analyze. Sentiment analysis of writing can quantitatively assess surgeon reviews to provide actionable feedback for surgeons to improve practice. The objective of this study is to quantitatively analyze large subset of written reviews of hand surgeons using sentiment analysis and report unbiased trends in words used to describe the reviewed surgeons and biases associated with surgeon demographic factors. Methods: Online written and star-rating reviews of hand surgeons were obtained from healthgrades.com and webmd.com . A sentiment analysis package was used to calculate compound scores of all reviews. Mann-Whitney U tests were performed to determine the relationship between demographic variables and average sentiment score of written reviews. Positive and negative word and word-pair frequency analysis was also performed. Results: A total of 786 hand surgeons’ reviews were analyzed. Analysis showed a significant relationship between the sentiment scores and overall average star-rated reviews ( r2 = 0.604, P ≤ .01). There was no significant difference in review sentiment by provider sex; however, surgeons aged 50 years and younger had more positive reviews than older ( P < .01). The most frequently used bigrams used to describe top-rated surgeons were associated with good bedside manner and efficient pain management, whereas those with the worst reviews are often characterized as rude and unable to relieve pain. Conclusions: This study provides insight into both demographic and behavioral factors contributing to positive reviews and reinforces the importance of pain expectation management.

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