Ontology-Based Opinion Mining

2013 ◽  
pp. 327-338
Author(s):  
Rajendra Akerkar ◽  
Terje Aaberge
Keyword(s):  
Text Mining ◽  
Opinion Mining ◽  
Shared Meaning ◽  
Linguistic Relations

In this chapter, the authors discuss an ontology-based approach to opinion mining exploiting the possibility to represent commonly shared meaning of linguistic relations by ontologies. The ontology definitions are used as a standard to which sentences extracted from texts are compared. Unlike conventional text mining, which is based on objective topics aiming to discover common patterns of user opinions from their textual statements automatically or semi-automatically, it will extract opinion from subjective locations.

scholarly journals Questions and features of automated social networks monitoring with support of intelligent user messages analysis.

2014 ◽  
Vol 2014 (4) ◽  
pp. 146-152 ◽  
Author(s):  
Александр Подвесовский ◽  
Aleksandr Podvesovskiy ◽  
Дмитрий Будыльский ◽  
Dmitriy Budylskiy
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Text Mining ◽  
Sentiment Analysis ◽  
Opinion Mining ◽  
Software Implementation ◽  
Network Data ◽  
Research Directions ◽  
Social Network Data ◽  
Monitoring Model

An opinion mining monitoring model for social networks introduced. The model includes text mining processing over social network data and uses sentiment analysis approach in particular. Practical usage results of software implementation and its requirements described as well as further research directions.

scholarly journals Online Realtime Sentiment Analysis Tweets by Utilizing Streaming API Features From Twitter

2019 ◽  
Vol 9 (1) ◽  
pp. 53
Author(s):  
Nfn Bahrawi
Keyword(s):  
Social Media ◽  
Text Mining ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Opinion Mining ◽  
Relevant Information ◽  
Processing Technique ◽  
Web Based ◽  
Analysis Process ◽  
Analysis System

<p class="JGI-AbstractIsi">Twitter is one of the social media that has a simple and fast concept, because short messages, news or information on Twitter can be more easily digested. This social media is also widely used as an object for researchers or industry to conduct sentiment analysis in the fields of social, economic, political or other fields. Opinion mining or also commonly called sentiment analysis is the process of analyzing text to get certain information in a sentence in the form of opinion. Sentiment analysis is one of the branches of the science of Text mining where text mining is a natural language processing technique and analytical method that is applied to text data to obtain relevant information. Public opinion or sentiment in social media twitter is very dynamic and fast changing, a real time sentiment analysis system is needed and it is automatically updated continuously so that changes can always be monitored, anytime and anywhere. This research builds a system so that it can analyze sentiment from twitter social media in realtime and automatically continuously. The results of the system trial succeeded in drawing data, conducting sentiment analysis and displaying it in graphical and web-based realtime and updated automatically. Furthermore, this research will be developed with a focus on the accuracy of the algorithms used in conducting the sentiment analysis process.</p>

scholarly journals Opinion Mining of Amazon Data for Alexa Review Analysis

2021 ◽  
Vol 9 (VII) ◽  
pp. 3814-3819
Author(s):  
Neetima Gautam
Keyword(s):  
Text Mining ◽  
Sentiment Analysis ◽  
Opinion Mining ◽  
Review Analysis ◽  
Text Information ◽  
Frame Work

The fast expansion in piles of unstructured literary information joined by multiplication of devices to investigate them has opened up extraordinary freedoms and difficulties for text mining research. The programmed naming of information is hard in light of the fact that individuals regularly express feelings in complex manners that are here and there hard to fathom. The marking interaction includes tremendous measure of endeavours and mislabelled datasets typically lead to erroneous choices. In this paper, we plan a frame work for sentiment analysis with opinion mining for the instance of Amazon Alexa. Most accessible datasets are not named which presents a great deal of works for scientists as tolls text information pre-preparing task is concerned. Also, supposition datasets are frequently profoundly area touchy and difficult to make since assumptions are sentiments like feelings, mentalities and conclusions that are ordinarily overflowing with phrases, sound to word imitations, homophones, phonemes, similar sounding word usages and abbreviations. The proposed system is named feeling extremity that naturally readies a supposition dataset for preparing and testing to extricate impartial assessments of inn administrations from surveys to find a reasonable AI calculation for the grouping segment of the structure.

A Method for Social Network Extraction From E-Government

2019 ◽  
Vol 11 (3) ◽  
pp. 37-55
Author(s):  
Rasim M. Alguliyev ◽  
Ramiz M. Aliguliyev ◽  
Gunay Y. Niftaliyeva (Iskandarli)
Keyword(s):  
Social Networks ◽  
Content Analysis ◽  
Social Network ◽  
Text Mining ◽  
Social Relationships ◽  
Opinion Mining ◽  
The State ◽  
Key Factors ◽  
Mining Technology ◽  
The Government

Nowadays, improvement of governance, ensuring security and timely detection of propaganda against the government are major problems of e-government. The extraction of hidden social networks operating against the state in e-government is one of the key factors to ensure the security in e-government. In this article, a method has been proposed for extracting hidden social networks to improve e-government management, prevent promotion against the government and ensure the security. In this approach, hidden social networks are extracted through the analysis of user's comments via opinion and text mining technologies. The authors assume that all comments are written in one language. Unlike previous methods, to detect social relationships between actors, content analysis technology, namely opinion mining technology was used in the proposed approach.

scholarly journals IMPLEMENTASI ANALISIS CLUSTERING DAN SENTIMEN DATA TWITTER PADA OPINI WISATA PANTAI MENGGUNAKAN METODE K-MEANS

2018 ◽  
Vol 4 (3) ◽  
pp. 189
Author(s):  
Yan Watequlis Syaifudin ◽  
Rizki Andi Irawan
Keyword(s):  
Support Vector Machine ◽  
Text Mining ◽  
Opinion Mining ◽  
Support Vector ◽  
Bahasa Indonesia

Analisis sentimen atau opinion mining merupakan topik riset dari cabang penelitian text mining. Fokus dari analisis sentimen adalah melakukan analisis opini dari suatu dokumen teks, sehingga membantu usaha untuk melakukan riset pasar atas opini publik. Data opini diperoleh dari jejaring sosial Twitter dalam Bahasa Indonesia dengan topik suatu pantai.Klasifikasi opini diperlukan untuk memudahkan pengguna dalam melihat opini positif, negatif, ataupun netral. Algoritma yang digunakan dalam klasifikasi adalah Support Vector Machine. Pada penelitian digunakan dataset dari 10 pantai yang ada di Indonesia sebanyak 500 tweet. Hasil akurasi dari klasifikasi menggunakan algoritma Support Vector Machine sebesar 74,39%. Selanjutnya data opini dari kuesioner ditambahkan untuk mengelompokkan pantai berdasarkan ketersediaan sumber daya, fasilitas, akses, kesiapan masyarakat, potensi pasar dan posisi pariwisata. Dalam proses pengelompokan data ini digunakan metode K-Means.

scholarly journals The Analisis Sentimen Sosial Media Twitter Dengan Algoritma Machine Learning Menggunakan Software R

2017 ◽  
Vol 6 (2) ◽  
pp. 85 ◽  
Author(s):  
Jaka Aulia Pratama ◽  
Yadi Suprijadi ◽  
Zulhanif Zulhanif
Keyword(s):  
Machine Learning ◽  
Text Mining ◽  
Opinion Mining ◽  
Barack Obama ◽  
Donald Trump

Media sosial adalah wadah untuk mengungkapkan opini terhadap suatu topik tertentu. Ketersediaan informasi dan opini dari para pengguna media sosial merupakan kumpulan dokumen data berupa teks yang amat sangat besar dan berguna untuk kepentingan penelitian maupun membuat suatu keputusan bagi pihak – pihak tertentu. Text Mining bisa didefinisikan sebagai proses penggalian informasi di mana pengguna berinteraksi dengan kumpulan dokumen dari waktu ke waktu dengan menggunakan suatu alat analisis. Analisis sentimen atau Opinion Mining adalah salah satu studi di bidang komputasi yang berhubungan dengan kasus publik mengenai opini, penilaian, sikap, dan emosi. Penelitian ini akan menggunakan metode Machine Learning pada analisis sentimen pengguna layanan jejaring sosial Twitter terhadap Donald Trump dan Barack Obama dalam 20000 tweets. Nilai akurasi metode Machine Learning yang diperoleh cukup tinggi yaitu 87.52% untuk Data Training dan 87.4% untuk Data Testing.

scholarly journals Mining Public Opinion about Economic Issues

2018 ◽  
Vol 9 (1) ◽  
pp. 18-28 ◽  
Author(s):  
Amir Karami ◽  
London S. Bennett ◽  
Xiaoyun He
Keyword(s):  
Social Media ◽  
Public Opinion ◽  
Text Mining ◽  
Opinion Mining ◽  
Social Media Data ◽  
Economic Issues ◽  
Election Prediction ◽  
Us Presidential Election ◽  
Mining Methods ◽  
Media Data

Opinion polls have been the bridge between public opinion and politicians in elections. However, developing surveys to disclose people's feedback with respect to economic issues is limited, expensive, and time-consuming. In recent years, social media such as Twitter has enabled people to share their opinions regarding elections. Social media has provided a platform for collecting a large amount of social media data. This article proposes a computational public opinion mining approach to explore the discussion of economic issues in social media during an election. Current related studies use text mining methods independently for election analysis and election prediction; this research combines two text mining methods: sentiment analysis and topic modeling. The proposed approach has effectively been deployed on millions of tweets to analyze economic concerns of people during the 2012 US presidential election.

Classification of Reviews on Mobile Phones Using Text Mining Techniques

2018 ◽  
Vol 7 (3.4) ◽  
pp. 163
Author(s):  
Livin Davis ◽  
V Vaidhehi
Keyword(s):  
Text Mining ◽  
Mobile Phones ◽  
Opinion Mining ◽  
Visual Representation ◽  
Research Work ◽  
Positive Opinion ◽  
Point Scale ◽  
Negative Opinion ◽  
Word Cloud

People register their opinion or feedback regarding the products in different forum. This research work is based on the classification of reviews regarding the different mobile phones. Dataset from Amazon pertaining to the opinions for mobile phones is used in this work. Opinion which is expressed as text is classified as positive opinion or a negative opinion using text mining techniques. Opinion mining helps to understand the customers in a better way. This work shows the visual representation of words by using word cloud and to classify the reviews on a two point scale. From the dataset, randomly 197 reviews are taken out of which 148 reviews are classified as positive, 49 reviews are classified as negative. 

scholarly journals Combined Text Mining: Fuzzy Clustering for Opinion Mining on the Traditional Culture Arts Work

2020 ◽  
Vol 11 (8) ◽  
Author(s):  
Elta Sonalitha ◽  
Anis Zubair ◽  
Priyo Dari ◽  
Salnan Ratih ◽  
Bambang Nurdewanto ◽  
...  
Keyword(s):  
Text Mining ◽  
Fuzzy Clustering ◽  
Opinion Mining ◽  
Traditional Culture
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