Recommendation Systems, Incorporating Sentiment Analysis with Specific Reference to the Academic Domain

2018 ◽  
Vol 6 (9) ◽  
pp. 17-22
Author(s):  
Anil Kumar ◽  
Sonal Chawla
Keyword(s):  
Sentiment Analysis ◽  
Recommendation Systems ◽  
Specific Reference

Sentiment Analysis Techniques for Social Media-Based Recommendation Systems

2020 ◽  
Author(s):  
Selvi Munuswamy ◽  
M. S. Saranya ◽  
S. Ganapathy ◽  
S. Muthurajkumar ◽  
A. Kannan
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Recommendation Systems ◽  
Analysis Techniques

scholarly journals Event Recommendation System using Hybrid Method Based on Mobile Device

2021 ◽  
Vol 6 (1) ◽  
pp. 107-116
Author(s):  
Dio Saputra Kudori
Keyword(s):  
Everyday Life ◽  
Collaborative Filtering ◽  
Sentiment Analysis ◽  
Mobile Device ◽  
Hybrid Method ◽  
Recommendation System ◽  
Recommendation Systems ◽  
Unique Character ◽  
Event Recommendation

In everyday life there are many events that are held. Theseeventuse various ways in term of announcing eventfor attracting people to come.Because there are many event that are held in everyday life,an event recommendation system can be implemented to provide event recommendations that are appropriate for the user. In developing event recommendation systems, there are many methods that can be used, the onethat frequently used is collaborative filtering. The event recommendation system has a unique character compared to other recommendation systems. This is because the event recommendation system doesn’t use the classic scenario of a recommendation system. In this study we tried to use a hybrid method that combines collaborative filteringwith sentiment analysis. The experiment show that the results of the event recommendations have an accuracy value of 82%. Itshows that the hybrid method can be utilized for developing event recommendation systems.

scholarly journals Sentiment Digitization Modeling for Recommendation System

2020 ◽  
Vol 12 (12) ◽  
pp. 5191
Author(s):  
Tae-Yeun Kim ◽  
Sung Bum Pan ◽  
Sung-Hwan Kim
Keyword(s):  
Collaborative Filtering ◽  
Sentiment Analysis ◽  
Recommendation System ◽  
Recommendation Systems ◽  
Quantitative Information ◽  
Personalized Recommendation ◽  
Rating Data ◽  
User Reviews ◽  
Lower Accuracy ◽  
Types Of Information

As the importance of providing personalized services increases, various studies on personalized recommendation systems are actively being conducted. Among the many methods used for recommendation systems, the most widely used is collaborative filtering. However, this method has lower accuracy because recommendations are limited to using quantitative information, such as user ratings or amount of use. To address this issue, many studies have been conducted to improve the accuracy of the recommendation system by using other types of information, in addition to quantitative information. Although conducting sentiment analysis using reviews is popular, previous studies show the limitation that results of sentiment analysis cannot be directly reflected in recommendation systems. Therefore, this study aims to quantify the sentiments presented in the reviews and reflect the results to the ratings; that is, this study proposes a new algorithm that quantifies the sentiments of user-written reviews and converts them into quantitative information, which can be directly reflected in recommendation systems. To achieve this, the user reviews, which are qualitative information, must first be quantified. Thus, in this study, sentiment scores are calculated through sentiment analysis by using a text mining technique. The data used herein are from movie reviews. A domain-specific sentiment dictionary was constructed, and then based on the dictionary, sentiment scores of the reviews were calculated. The collaborative filtering of this study, which reflected the sentiment scores of user reviews, was verified to demonstrate its higher accuracy than the collaborative filtering using the traditional method, which reflects only user rating data. To overcome the limitations of the previous studies that examined the sentiments of users based only on user rating data, the method proposed in this study successfully enhanced the accuracy of the recommendation system by precisely reflecting user opinions through quantified user reviews. Based on the findings of this study, the recommendation system accuracy is expected to improve further if additional analysis can be performed.

scholarly journals Movie Recommendation Systems using Sentiment Analysis and Cosine Similarity

2021 ◽  
Vol 7 (01) ◽  
pp. 16-22
Author(s):  
Raghav Mehta and Shikha Gupta
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Recommendation Systems ◽  
Machine Learning Algorithms ◽  
Cosine Similarity ◽  
Rapid Rate ◽  
The Past ◽  
Movie Recommendation ◽  
Tv Shows ◽  
Online Streaming

As Artificial Intelligence and Machine Learning is growing at a rapid rate over the past few years, so is the amount of data increasing exponentially on the internet. Due to this people find it difficult to choose the exact information they are looking for , learners find it difficult to suggest users exactly what they require. Here comes Recommendation Systems into picture to guide users towards the information according to their preferences. In context of Recommendation of Movies and TV shows on Online Streaming platforms ,this paper is aimed to explain making and implementation of Movie Recommendation Systems Using Machine Learning Algorithms, Sentiment Analysis and Cosine Similarity

Study of Sentiment of Governor's Election Opinion in 2018

2018 ◽  
pp. 231-238
Author(s):  
Agung Eddy Suryo Saputro ◽  
Khairil Anwar Notodiputro ◽  
Indahwati A
Keyword(s):  
Sentiment Analysis ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Addition Method ◽  
Sentiment Mining ◽  
Positive Sentiment ◽  
Trending Topics ◽  
Election News

In 2018, Indonesia implemented a Governor's Election which included 17 provinces. For several months before the Election, news and opinions regarding the Governor's Election were often trending topics on Twitter. This study aims to describe the results of sentiment mining and determine the best method for predicting sentiment classes. Sentiment mining is based on Lexicon. While the methods used for sentiment analysis are Naive Bayes and C5.0. The results showed that the percentage of positive sentiment in 17 provinces was greater than the negative and neutral sentiments. In addition, method C5.0 produces a better prediction than Naive Bayes.

An historical analysis of species references in American English

Corpora ◽  
2019 ◽  
Vol 14 (3) ◽  
pp. 327-349
Author(s):  
Craig Frayne
Keyword(s):  
Environmental Change ◽  
Sentiment Analysis ◽  
Quantitative Methods ◽  
English Language ◽  
Language Use ◽  
American English ◽  
Historical Analysis ◽  
The Past ◽  
Corpus Studies ◽  
Google Books

This study uses the two largest available American English language corpora, Google Books and the Corpus of Historical American English (coha), to investigate relations between ecology and language. The paper introduces ecolinguistics as a promising theme for corpus research. While some previous ecolinguistic research has used corpus approaches, there is a case to be made for quantitative methods that draw on larger datasets. Building on other corpus studies that have made connections between language use and environmental change, this paper investigates whether linguistic references to other species have changed in the past two centuries and, if so, how. The methodology consists of two main parts: an examination of the frequency of common names of species followed by aspect-level sentiment analysis of concordance lines. Results point to both opportunities and challenges associated with applying corpus methods to ecolinguistc research.

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