scholarly journals PENGEMBANGAN INTELLIGENT DATA COLLECTOR UNTUK ANALISIS BIG DATA ARTIKEL BERITA ONLINE

2018 ◽  
Vol 3 (1) ◽  
pp. 49-59
Author(s):  
Zul Indra ◽  
Liza Trisnawati
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Sentiment Analysis ◽  
Opinion Mining ◽  
Text Summarization ◽  
Data Collector

Big data  telah menjadi salah satu topik yg paling menarik dalam dunia teknologi informasi sekarang ini. Salah satu sumber big data yang tersedia dan bebas diakses adalah artikel berita online. Dalam sehari, sebuah situs berita populer bisa menghasilkan lebih dari 100 artikel berita baru. Bayangkan berapa banyak jumlah halaman berita yang tersedia untuk kita baca sekarang ini. Sementara itu, tahap awal untuk melakukan analisis big data terhadap artikel berita online adalah data storing dan preprocessing. Berdasarkan pemikiran tersebut maka perlu dikembangkan suatu aplikasi yang bisa mengumpulkan artikel berita online secara otomatis untuk kemudian di analisis lebih lanjut. Penelitian ini bermaksud mengembangkan suatu aplikasi yang diberi nama dengan intelligent data collector (IDC) yang memudahkan kita untuk mengumpulkan artikel berita online. Aplikasi IDC ini mengumpulkan artikel berita online kemudian melakukan preprocessing terhadap artikel-artikel tersebut dan menyimpannya dalam database lokal. Database ini kemudian bisa digunakan lebih lanjut untuk berrbagai macam data mining proses seperti opinion mining (sentiment analysis), topic classification, text summarization dan lain sebagainya.

scholarly journals Sentiment Analysis using Optimized Feature Sets in Different Facebook/Twitter Dataset Domains using Big Data

2022 ◽  
pp. 64-70
Author(s):  
Mohammed Ibrahim Al-mashhadani ◽  
Kilan M. Hussein ◽  
Enas Tariq Khudir ◽  
Muhammad ilyas
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Online Social Networks ◽  
Opinion Mining ◽  
Real Life ◽  
Online Reviews ◽  
Support Vector ◽  
Main Task

Now days, in many real life applications, the sentiment analysis plays very vital role for automatic prediction of human being activities especially on online social networks (OSNs). Therefore since from last decade, the research on opinion mining and sentiment analysis is growing with increasing volume of online reviews available over the social media networks like Facebook OSNs. Sentiment analysis falls under the data mining domain research problem. Sentiment analysis is kind of text mining process used to determine the subjective attitude like sentiment from the written texts and hence becoming the main research interest in domain of natural language processing and data mining. The main task in sentiment analysis is classifying human sentiment with objective of classifying the sentiment or emotion of end users for their specific text on OSNs. There are number of research methods designed already for sentiment analysis. There are many factors like accuracy, efficiency, speed etc. used to evaluate the effectiveness of sentiment analysis methods. The MapReduce framework under the domain of big-data is used to minimize the speed of execution and efficiency recently with many data mining methods. The sentiment analysis for Facebook OSNs messages is very challenging tasks as compared to other sentiment analysis because of misspellings and slang words presence in twitter dataset. In this paper, different solutions recently presented are discussed in detail. Then proposed the new approach for sentiment analysis based on hybrid features extraction methods and multi-class Support Vector Machine (SVM). These algorithms are designed using the Big-data techniques to optimize the performance of sentiment analysis

Data Mining Approaches for Sentiment Analysis in Online Social Networks (OSNs)

2022 ◽  
pp. 116-141
Author(s):  
Praneeth Gunti ◽  
Brij B. Gupta ◽  
Elhadj Benkhelifa
Keyword(s):  
Data Mining ◽  
Social Networks ◽  
Big Data ◽  
Sentiment Analysis ◽  
Online Social Networks ◽  
Opinion Mining ◽  
Technical Problems ◽  
Potential Study ◽  
Technical Issues

IoT technology and the widespread usage of public networking platforms and apps also made it possible to use data mining in extracting useful perspectives from unorganised knowledge. In the age of big data, opinion mining may be applied as a valuable way in order to classify views into various sentiment and in general to determine the attitude of the population. Other methods to OSA have been established over the years in various datasets and evaluated in varying conditions. In this respect, this chapter highlights the scope of OMSA strategies and forms of implementing OMSA principles. Besides technological issues of OMSA, this chapter also outlined both technical problems regarding its production and non-technical issues regarding its use. There are obstacles for potential study.

scholarly journals Sentiment Analysis and Summarization of Social Media Content using Topic Modeling

2020 ◽  
Vol 9 (3) ◽  
pp. 209-212
Keyword(s):  
Data Mining ◽  
Social Media ◽  
Sentiment Analysis ◽  
Topic Modeling ◽  
Opinion Mining ◽  
Text Documents ◽  
Topic Identification ◽  
Key Factor ◽  
Polarity Classification ◽  
Using Data

Explosion of Web 2.0 had made different social media platforms like Facebook, Twitter, Blogs, etc a data hub for the task of Data Mining. Sentiment Analysis or Opinion mining is an automated process of understanding an opinion expressed by customers. By using Data mining techniques, sentiment analysis helps in determining the polarity (Positive, Negative & Neutral) of views expressed by the end user. Nowadays there are terabytes of data available related to any topic then it can be advertising, politics and Survey Companies, etc. CSAT (Customer Satisfaction) is the key factor for this survey companies. In this paper, we used topic modeling by incorporating a LDA algorithm for finding the topics related to social media. We have used datasets of 900 records for analysis. By analysis, we found three important topics from Survey/Response dataset, which are Customers, Agents & Product/Services. Results depict the CSAT score according to Positive, Negative and Neutral response. We used topic modeling which is a statistical modeling technique. Topic modeling is a technique for categorization of text documents into different topics. This approach helps in better summarization of data according to the topic identification and depiction of polarity classification of sentiments expressed.

scholarly journals Towards Application of Speech Act Theory to Opinion Mining

2016 ◽  
pp. 33-44
Author(s):  
Agnieszka Magdalena Pluwak
Keyword(s):  
Data Mining ◽  
Sentiment Analysis ◽  
Speech Acts ◽  
Opinion Mining ◽  
Speech Act ◽  
Speech Act Theory ◽  
Market Demand ◽  
Traditional Methods ◽  
Indirect Evaluation

Towards the Application of Speech Act Theory to Opinion MiningThe paper refers to the pragmatics’ perspective on opinion mining in Polish and English, inspired by the discrepancy between the coverage of sentiment analysis and the market demand. An analysis of speech acts expressed in opinion texts reveals that almost half of all opinions include ways of indirect evaluation that might not get extracted while applying traditional methods of sentiment analysis based on direct evaluative vocabulary and polarity lexicons. Coding of sentiment with respect to speech acts could vastly broaden data mining results within an NLP-system. O zastosowaniu teorii aktów mowy w ekstrakcji danych z tekstów opinii internetowychJedno z aktualnych zagadnień językoznawstwa komputerowego, jakim jest automatyczne badanie wydźwięku wypowiedzi, nie uwzględniło dotychczas w wystarczającym stopniu pragmatyki językoznawczej, np. aktów mowy Austina (1961) i Searla (1969), a zatem również implicytnych sposobów wyrażania ewaluacji. Tymczasem podejście od pragmatyki ku konstrukcjom przełożonym na reguły programistyczne umożliwiłoby nie tylko szersze spojrzenie na analizę sentymentu, ale też zbliżyłoby automat do sposobu, w jaki odbiera go człowiek. W szczególności chodzi tu sposoby wyrażania (nie)zadowolenia wykraczające poza poziom leksykalny (bez nacechowanej negatywnie leksyki), typu Nigdy więcej tam nie pójdę.Artykuł prezentuje: 1. aktualne podejścia do analizy wydźwięku w lingwistyce komputerowej, 2. propozycję zastosowania podejścia pragmatycznego, 3. wyniki badania próbki tekstów opinii internetowych pod kątem występowania w nich aktów mowy, 4. propozycję utworzenia reguł ekstrakcji danych na ich podstawie. Zaprezentowane podejście zakłada hipotezę wtórnej oralności, czyli tego, że język opinii jest zapisanym językiem mówionym.

scholarly journals A real-time big data sentiment analysis for iraqi tweets using spark streaming

2020 ◽  
Vol 9 (4) ◽  
pp. 1411-1419
Author(s):  
Nashwan Dheyaa Zaki ◽  
Nada Yousif Hashim ◽  
Yasmin Makki Mohialden ◽  
Mostafa Abdulghafoor Mohammed ◽  
Tole Sutikno ◽  
...  
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Sentiment Analysis ◽  
Real Time ◽  
Data Science ◽  
Opinion Mining ◽  
Data Streaming ◽  
Machine Learning Applications ◽  
Learning Research

The scale of data streaming in social networks, such as Twitter, is increasing exponentially. Twitter is one of the most important and suitable big data sources for machine learning research in terms of analysis, prediction, extract knowledge, and opinions. People use Twitter platform daily to express their opinion which is a fundamental fact that influence their behaviors. In recent years, the flow of Iraqi dialect has been increased, especially on the Twitter platform. Sentiment analysis for different dialects and opinion mining has become a hot topic in data science researches. In this paper, we will attempt to develop a real-time analytic model for sentiment analysis and opinion mining to Iraqi tweets using spark streaming, also create a dataset for researcher in this field. The Twitter handle Bassam AlRawi is the case study here. The new method is more suitable in the current day machine learning applications and fast online prediction. 

scholarly journals Sentiment Trend Analysis of Big Data

2019 ◽  
Vol 8 (10) ◽  
pp. 59-64
Keyword(s):  
Decision Making ◽  
Social Media ◽  
Big Data ◽  
Sentiment Analysis ◽  
Real Time ◽  
Language Processing ◽  
Opinion Mining ◽  
Data Sources ◽  
Product Reviews

Various fields like Text Mining, Linguistics, Decision Making and Natural Language Processing together form the basis for Opinion Mining or Sentiment Analysis. People share their feelings, observations and thoughts on social media, which has emerged as a powerful tool for rapidly growing enormous repository of real time discussions and thoughts shared by people. In this paper, we aim to decipher the current popular opinions or emotions from various sources, hence, contributing to sentiment analysis domain. Text from social media, blogs and product reviews are classified according to the sentiment they project. We re-examine the traditional processes of sentiment extraction, to incorporate the increase in complexity and number of the data sources and relevant topics, while re-populating the meaning of sentiment. Working across and within numerous streams of social media, expression of sentiment and classification of polarity is re-examined, thereby redefining and enhancing the realm of sentiment. Numerous social media streams are analyzed to build datasets that are topical for each stream and are later polarized according to their sentiment expression. In conclusion, defining a sentiment and developing tools for its analysis in real time of human idea exchange is the motive.

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