scholarly journals Forecasting Stock Price Movements Based on Opinion Mining and Sentiment Analysis: An Application of Support Vector Machine and Twitter Data

2020 ◽  
Vol 15 (3) ◽  
pp. 235-251
Author(s):  
Babak Sohrabi ◽  
Ahmad Khalili Jafarabad ◽  
Ardalan Hadizadeh ◽  
◽  
◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Sentiment Analysis ◽  
Stock Price ◽  
Opinion Mining ◽  
Support Vector ◽  
Twitter Data ◽  
Price Movements ◽  
Stock Price Movements

scholarly journals Aspect Term Extraction for Aspect Based Opinion Mining

2019 ◽  
Vol 8 (11) ◽  
pp. 2228-2233
Keyword(s):  
Support Vector Machine ◽  
Sentiment Analysis ◽  
Random Fields ◽  
Opinion Mining ◽  
Nearest Neighbor ◽  
Conditional Random Fields ◽  
International Workshop ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Term Extraction

Opinion Mining (OM) is also called as Sentiment Analysis (SA). Aspect Based Opinion Mining (ABOM) is also called as Aspect Based Sentiment Analysis (ABSA). In this paper, three new features are proposed to extract the aspect term for Aspect Based Sentiment Analysis (ABSA). The influence of the proposed features is evaluated on five classifiers namely Decision Tree (DT), Naive Bayes (NB), K-Nearest Neighbor (KNN), Support Vector Machine (SVM) and Conditional Random Fields (CRF). The proposed features are evaluated on the Two datasets on Restaurant and Laptop domains available in International Workshop on Semantic Evaluation 2014 i.e. SemEval 2014. The influence of proposed features is evaluated using Precision, Recall and F1 measures. The proposed features are highly influencing for aspect term extraction on classifiers. The performance of SVM and CRF classifiers with proposed features is more influencing for aspect term extraction compared with NB, DT and KNN classifiers.

scholarly journals Sentiment Analysis for Twitter Data

2020 ◽  
Vol 9 (7S) ◽  
pp. 98-101
Keyword(s):  
Sentiment Analysis ◽  
Social Networking Sites ◽  
Opinion Mining ◽  
Daily Basis ◽  
Support Vector ◽  
Twitter Data ◽  
Internet Users ◽  
Political Views ◽  
Large Numbers ◽  
Machine Learning Approach

With the advancements in web technology and its growth, there's an incredible volume of information present everywhere on the net for internet users and plenty more data is generated on a daily basis. Internet emerged as place for exchanging ideas, sharing opinions, online learning and political views. Social networking sites such as Facebook, Twitter, are rapidly growing as the users are allowed to post and revel their views on various topics, and can discussion with different groups and communities, or post messages across the world. In the area of sentiment analysis large numbers of researchers are working. The main focus is on twitter data for sentiment analysis, that's helpful to research the info within the tweets,where opinions are heterogeneous, highly unstructured, and are either positive,or negative, or neutral.in many cases. In this paper, we provide a study and comparative analysis of existing techniques used for opinion mining through machine learning approach. Naive Bayes & Support Vector Machine, we provide research on twitter data.

A New SVM Method for Recognizing Polarity of Sentiments in Twitter

2017 ◽  
pp. 281-291 ◽  
Author(s):  
Sanjiban Sekhar Roy ◽  
Marenglen Biba ◽  
Rohan Kumar ◽  
Rahul Kumar ◽  
Pijush Samui
Keyword(s):  
Social Networks ◽  
Support Vector Machine ◽  
Public Relations ◽  
Social Networking ◽  
Sentiment Analysis ◽  
High Accuracy ◽  
Support Vector ◽  
Online Social Networking ◽  
Web Page ◽  
Twitter Data

Online social networking platforms, such as Weblogs, micro blogs, and social networks are intensively being utilized daily to express individual's thinking. This permits scientists to collect huge amounts of data and extract significant knowledge regarding the sentiments of a large number of people at a scale that was essentially impractical a couple of years back. Therefore, these days, sentiment analysis has the potential to learn sentiments towards persons, object and occasions. Twitter has increasingly become a significant social networking platform where people post messages of up to 140 characters known as ‘Tweets'. Tweets have become the preferred medium for the marketing sector as users can instantly indicate customer success or indicate public relations disaster far more quickly than a web page or traditional media does. In this paper, we have analyzed twitter data and have predicted positive and negative tweets with high accuracy rate using support vector machine (SVM).

scholarly journals Sentiment Analysis on E-commerce Product using Machine Learning and Combination of TF-IDF and Backward Elimination

2020 ◽  
Vol 8 (6) ◽  
pp. 2862-2867
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Feature Selection ◽  
Sentiment Analysis ◽  
Opinion Mining ◽  
Classification Performance ◽  
Support Vector ◽  
Product Reviews ◽  
Feature Selection Technique ◽  
Backward Elimination

E-commerce is a website or mobile application platform that help people to buy products. Before purchasing the product, customer will decide to buy it or not by reading the review from previous buyer. There is a problem that there are a lot of review so it will take a long time for customer to read it all. This research will be using sentiment analysis method to classify the review data. Sentiment analysis or opinion mining is a machine learning approach to classify and analyse texts or documents about human’s sentiments, emotions, and opinions. In this research, sentiment analysis was used to classify product reviews from e-commerce websites into positive or negative classes. The results could be processed further and be used to summarize customers' opinions about a certain product without reading every single review. The goal of this research is to optimize classification performance by using feature selection technique. Terms Frequency-Inverse Document Frequency (TF-IDF) feature extraction, Backward Elimination feature selection, and five different classifiers (Naïve Bayes, Support Vector Machine, K-Nearest Neighbour, Decision Tree, Random Forest) were used in analysing the sentiment of the reviews. In this research, the dataset used are Indonesian language and classified into two classes(positive and negative). The best accuracy is achieved by using TF-IDF, Backward Elimination and Support Vector Machine (SVM) with a score of 85.97%, which increases by 7.91% if compared to the process without feature selection. Based on the results, Backward Elimination feature selection succeeded in improving all performance for all classifiers used in this research.

scholarly journals A Comparative Study of Different Classification Techniques for Sentiment Analysis

2020 ◽  
Vol 11 (1) ◽  
pp. 49-57
Author(s):  
Soumadip Ghosh ◽  
Arnab Hazra ◽  
Abhishek Raj
Keyword(s):  
Social Media ◽  
Support Vector Machine ◽  
Logistic Regression ◽  
Decision Tree ◽  
Comparative Study ◽  
Sentiment Analysis ◽  
Opinion Mining ◽  
Support Vector ◽  
Product Reviews ◽  
Accurate Model

Sentiment analysis denotes the analysis of emotions and opinions from text. The authors also refer to sentiment analysis as opinion mining. It finds and justifies the sentiment of the person with respect to a given source of content. Social media contain vast amounts of the sentiment data in the form of product reviews, tweets, blogs, and updates on the statuses, posts, etc. Sentiment analysis of this largely generated data is very useful to express the opinion of the mass in terms of product reviews. This work is proposing a highly accurate model of sentiment analysis for reviews of products, movies, and restaurants from Amazon, IMDB, and Yelp, respectively. With the help of classifiers such as logistic regression, support vector machine, and decision tree, the authors can classify these reviews as positive or negative with higher accuracy values.

scholarly journals An Enhanced Approach for Sentiment Analysis Using Association Rule Mining

2021 ◽  
Vol 9 (12) ◽  
pp. 913-918
Author(s):  
Abhishek Sharma
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Sentiment Analysis ◽  
Association Rule ◽  
Association Rule Mining ◽  
Opinion Mining ◽  
Support Vector ◽  
Rule Mining ◽  
The People ◽  
Baseline Approach

Abstract: In today’s world social networking platforms like Facebook, YouTube, twitter etc. are a great source of communication for internet users and loaded with large number of emotions, views and opinions of the people. Sentiment analysis is the study of attitudes, emotions and opinions of the people and is also known as opinion mining. Sentiment analysis is used to find the opinion i.e. negative or positive about a particular subject. In this paper an Enhanced sentiment analysis approach is presented by using the Association rule mining i.e. Apriori and machine learning approach such as Support Vector Machine. The Enhanced approach is compared with the baseline approach, on accuracy, precision, recall, and F1-score measures. The Enhanced approach for sentiment analysis is implemented using the R programming language. The Enhanced approach shows better performance in comparison to the baseline approach. Keyword: Sentiment Analysis, Opinion Mining, Support Vector Machine, Association Rule Mining, Machine Learning

Prediksi Harga Saham Menggunakan Sentimen Pilkada DKI Jakarta 2017 Dengan Algoritma Support Vector Machine

Repositor ◽  
2020 ◽  
Vol 2 (12) ◽  
pp. 1623
Author(s):  
Muhammad Fadliansyah ◽  
Setio Basuki ◽  
Yufis Azhar
Keyword(s):  
Social Media ◽  
Support Vector Machine ◽  
Sentiment Analysis ◽  
Stock Price ◽  
Support Vector ◽  
Support Vector Machine Algorithm ◽  
Target Class ◽  
Text Data ◽  
Central Data ◽  
Extract Information

AbstrakTwitter merupakan salah satu sosial media yang paling banyak dipakai di Indonesia, tidak hanya sebagai sarana berbagi informasi terkait hal – hal pribadi tetapi juga bisa berupa opini terhadap suatu topik. Tidak hanya sebagai pusat infromasi, twitter juga bisa digunakan sebagai pusat data berupa teks. Pilkada DKI Jakarta 2017 merupakan salah satu topik yang menarik untuk di bahas. Tidak hanya sebagai penentu kepemimpinan Jakarta untuk 5 tahun kedepan, tetapi karena pengaruh yang dimilikinya terhadap beberapa sektor di Indoensia. Tweet yang membahas topik Pilkada DKI Jakarta 2017 bisa diolah untuk mendapatkan informasi yang berguna, misalnya sentimen yang terjadi selama peristiwa politik ini terjadi. Sentimen yang didapat bisa digunakan dalam prediksi harga saham selama masa Pilkada. Untuk bisa mendapatkan sentimen dari data teks dari twitter, sentiment anaylsis digunakan untuk mengekstrak informasi dari tweet yang sudah dikumpulkan. Untuk melakukan sentiment analysis, algoritma support vector machine dipakai untuk mengklasifikasikan tweet kedalam target kelas. Hasil dari klasifikasi sentimen digunakan sebagai salah satu pembobot dalam regresi linier untuk memprediksi harga saham. Hasil dari pengujian menunjukkan bahwa penggunaan sentimen Pilkada DKI Jakarta 2017 untuk memprediksi harga saham cukup baik. Dimana nilai RMSE yang didapat oleh masing-masing saham bervariasi karena saham-saham yang dipilih berasal dari sektor yang berbeda. BBRI 58.974, SRTG 101.188, WIKA 52.042, ADHI 93.420 dan APLN 17.342.Abstract Twitter is one of the most widely used social media in Indonesia, not only as a means of sharing information related to personal matters but also as information. Not only as a center of information, twitter can also be as central data in the form of text. DKI Jakarta Election 2017 is one of the interesting topics to discuss. Not only as a determinant of Jakarta's leadership for the next 5 years, but because of the influence it has had on several sectors in Indonesia. A Tweet that discusses the topic of the 2017 DKI Jakarta Regional Election can be processed to get useful information, for example sentiments that occur during times. Sentiment that can be done in the context of prices during the election period. To be able to get sentiments from text data from twitter, anaylsis sentiment is to extract information from tweets that have been collected. To do sentiment analysis, the support vector machine algorithm is used to classify tweets in the target class. Results from the basis of sentiment as one weight in linear regression to predict prices. The results of the test show that the use of the DKI Jakarta Regional Election sentiment 2017 is to predict the stock price to be quite good. Where is the RMSE value that can be found by each different sector. BBRI 58,974, SRTG 101,188, WIKA 52,042, ADHI 93,420 and APLN 17,342.

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