Online ISSN: 2515-8260

Keywords : Sentiment Analysis


Clustering Analysis from Universities in Indonesia based on Sentiment Analysis

Hendra Achmadi; Isana Meranga; Dewi Wuisan; Irwan Suarly; I Gusti Anom Yudistira; Rudy Pramono

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 10, Pages 1466-1481

There are two kind of source to determine the quality for a good university in Indonesia. First from university cluster which is publish from Ministry of Research, Technology and Higher Education issued a clustering list of Indonesian universities, the second source of data from social media, such as Twitter. In this research we use Text Mining and Data Mining Methodology to build a sentiment analysis from 50100 Tweet to assess 501 university using Python and special library in Python for Natural Language Processing a sentiment analysis , which is join the university clustering from Ministry of Research, Technology and Higher Education, so it will produce the positive, neutral and negative sentiment for each 501 universities in 2020. The next process by using R STUDIO, the process classification is continued by using K-Means, the process can be devided into two step , step 1 it will process 501 dataset university and it will build 5 cluster and secondly the similarities between Netizen cluster and cluster from Ministry of Research, Technology and Higher Education is 37 %, and step 2 after cleansing the 0 value, the result is 169 universites the similarities between Netizen cluster and cluster from Ministry of Research, Technology and Higher Education is 37 % before and after data cleansing was the same. The novelty knowledge or research finding can be derived from Netizen, firstly, the cluster can be derived based on Positive Sentiment,. Secondly, the cluster from Netizen and Cluster from Directorate General of Higher Education, Ministry of Education and Culture of higher education in Indonesia is only match around 37 % with cluster form Directorate General of Higher Education. And after data cleansing from 169 university was only match around 33 %..

Survey On Aspect Based Sentiment Analysis Using Machine Learning Techniques

Syam Mohan E; R. Sunitha

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 10, Pages 1664-1684

Web 2.0 facilitates the expression of views through diverse Internet applications which serve as a rich source of information. The textual expressions have latent information that when processed and analysed reveal the sentiment of the user/people. This is known as sentiment analysis, which is the process of computationally extracting the opinions and viewpoints from textual data and it is also known as opinion mining, review mining or attitude mining, etc. Aspect-level sentiment analysis is one among the three main types of sentiment analysis, where granule level processing takes place in which the different aspects of entities are harnessed to identify the sentiment orientations. The emergence of machine learning and deep learning techniques has made a striking mark towards aspect-oriented sentiment analysis. This paper presents a survey and review of different works from the recent literature on aspect-based sentiment analysis done using machine learning techniques.

Stock Prediction using Sentiment analysis and Long Short Term Memory

Harsh Panday; V. Vijayarajan; Anand Mahendran; A. Krishnamoorthy; V.B. Surya Prasath

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 2, Pages 5060-5069

The Stock market is a shambolic place for prediction as there are plenty of factors that affect the stock market simultaneously. Numerous studies have been conducted regarding this field, in hopes that one day accurate stock values can be predicted. This paper introduces a hybrid algorithm that incorporates Twitter sentiment analysis and Long Short Term Memory to predict next day closing values of a stock. Our proposed algorithm exploits the temporal correlation between public sentiment and its effect on stock values. We use Part-of-speech tagging to perform sentiment analysis and Long Short Term Memory for foretelling the next day closing price of the stock, both of these combined gives us a decent picture regarding the future of the stock.

Women Protection Analysis Based On Twitter Data Using Ml

Raparthi Shravya; Dr.P. Neelakantan

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 7, Pages 5820-5825

Girls and Women have been encountering a ton of savagery and badgering in broad daylight places in different urban communities beginning from following and prompting inappropriate behaviour or rape. This paper examines essential centres around the function of web-based media in advancing the security of ladies in different areas with exceptional reference to the part of online media sites and applications including Twitter stage Facebook and Instagram. This paper additionally focuses around how a feeling of obligation on part of culture can be built up the basic Indian individuals. Tweets on Twitter which typically contains pictures and text and furthermore composed messages and statements which centres around the security of ladies in different urban areas can be utilized to peruse a message among the Youth Culture and instruct individuals to make exacting move and rebuff the individuals who disturb the ladies. Twitter and other Twitter handles which incorporate hash label messages that are generally spread over the entire globe as a stage for ladies to communicate their perspectives about how they feel while we go out for work or travel in a public vehicle and what is the condition of their brain when they are encircled by obscure men and if these ladies have a sense of security? By analyzing the tweets polarity from the Twitter API. In Further improvements, we can use it in any Social Media Platform.