Mining User-Generated Content for Social Research and Other Applications

2013 ◽  
pp. 230-264
Author(s):  
Rafael E. Banchs ◽  
Carlos G. Rodríguez Penagos
Keyword(s):  
Language Processing ◽  
Question Answering ◽  
Opinion Mining ◽  
Social Research ◽  
User Generated Content ◽  
Processing Technologies ◽  
Dialogue Management ◽  
Misbehavior Detection ◽  
New Research ◽  
The Web

The main objective of this chapter is to present a general overview of the most relevant applications of text mining and natural language processing technologies evolving and emerging around the Web 2.0 phenomenon (such as automatic categorization, document summarization, question answering, dialogue management, opinion mining, sentiment analysis, outlier identification, misbehavior detection, and social estimation and forecasting) along with the main challenges and new research opportunities that are directly and indirectly derived from them.

Mining User-Generated Content for Social Research and Other Applications

2013 ◽  
pp. 1945-1979
Author(s):  
Rafael E. Banchs ◽  
Carlos G. Rodríguez Penagos
Keyword(s):  
Language Processing ◽  
Question Answering ◽  
Opinion Mining ◽  
Social Research ◽  
User Generated Content ◽  
Processing Technologies ◽  
Dialogue Management ◽  
Misbehavior Detection ◽  
New Research ◽  
The Web

The main objective of this chapter is to present a general overview of the most relevant applications of text mining and natural language processing technologies evolving and emerging around the Web 2.0 phenomenon (such as automatic categorization, document summarization, question answering, dialogue management, opinion mining, sentiment analysis, outlier identification, misbehavior detection, and social estimation and forecasting) along with the main challenges and new research opportunities that are directly and indirectly derived from them.

Multi-Distribution Characteristics Based Chinese Entity Synonym Extraction from The Web

2019 ◽  
Vol 15 (3) ◽  
pp. 42-63
Author(s):  
Xiuxia Ma ◽  
Xiangfeng Luo ◽  
Subin Huang ◽  
Yike Guo
Keyword(s):  
Language Processing ◽  
Query Expansion ◽  
Question Answering ◽  
Distribution Characteristics ◽  
Activation Model ◽  
Ranking Problem ◽  
Data Sparseness ◽  
Parallel Structures ◽  
Improve Accuracy ◽  
The Web

Entity synonyms play an important role in natural language processing applications, such as query expansion and question answering. There are three main distribution characteristics in web texts:1) appearing in parallel structures; 2) occurring with specific patterns in sentences; and 3) distributed in similar contexts. The first and second characteristics rely on reliable prior knowledge and are susceptive to data sparseness, bringing high accuracy and low recall to synonym extraction. The third one may lead to high recall but low accuracy, since it identifies a somewhat loose semantic similarity. Existing methods, such as context-based and pattern-based methods, only consider one characteristic for synonym extraction and rarely take their complementarity into account. For increasing recall, this article proposes a novel extraction framework that can combine the three characteristics for extracting synonyms from the web, where an Entity Synonym Network (ESN) is built to incorporate synonymous knowledge. To improve accuracy, the article treats synonym detection as a ranking problem and uses the Spreading Activation model as a ranking means to detect the hard noise in ESN. Experimental results show the proposed method achieves better accuracy and recall than the state-of-the-art methods.

scholarly journals Opinion Mining for the Customer Feedback using TextBlob

2020 ◽  
pp. 72-76
Author(s):  
Praveen Gujjar J ◽  
Prasanna Kumar H R
Keyword(s):  
Decision Making ◽  
Language Processing ◽  
Opinion Mining ◽  
The Internet ◽  
Business Decision ◽  
Customer Feedback ◽  
Internet Users ◽  
Enormous Amount ◽  
The Web ◽  
Natural Language Processing Task

Evolution in the field of web technology has made an enormous amount of data available in the web for the internet users. These internet users give their useful feedback, comments, suggestion or opinion for the available product or service in the web. User generated data are very essential to analyze for business decision making. TextBlob is one of the simple API offered by python library to perform certain natural language processing task. This paper proposed a method for analyzing the opinion of the customer using TextBlob to understand the customer opinion for decision making. This paper, provide a result for aforesaid data using TextBlob API using python. The paper includes advantages of the proposed technique and concludes with the challenges for the marketers when using this technique in their decision-making.

scholarly journals Twitter Sentiment Analysis on Government Law Using Real Time Data

2021 ◽  
pp. 200-205
Author(s):  
Sujata Patil ◽  
Bhavesh Wagh ◽  
Aditya Bhinge ◽  
Aakash Sahal ◽  
Prof. Madhav Ingale
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Opinion Mining ◽  
Central Government ◽  
Research Work ◽  
Research Area ◽  
User Generated Content ◽  
Time Data ◽  
Social Data

Social media monitoring has been growing day by day so analyzing social data plays an important role in knowing people's behavior. So we are analyzing Social data such as Twitter Tweets using sentiment analysis which checks the opinion of people related to government schemes that are announced by the Central Government. This paper-based is on social media Twitter datasets of particular schemes and their polarity of sentiments. The popularity of the Internet has been rapidly increased. Sentiment analysis and opinion mining is the field of study that analyses people's opinions, sentiments, evaluations, attitudes, and emotions from written language. User-generated content is highly generated by users. The growing importance of sentiment analysis coincides with the growth of social media such as reviews, forum discussions, blogs, micro-blogs, Twitter, and social networks. It is difficult to analyze or summarize user-generated content. Most of the users write their opinions, thoughts on blogs, social media sites, E-commerce sites, etc. So these contents are very important for individuals, industry, government, and research work to make decisions. This Sentiment analysis and opinion mining research is a hot research area that comes under Natural Language processing. We plot and calculate numbers of positive, negative, and neutral tweets from each event.

scholarly journals NELL’s subcategories from a question answering environment

2018 ◽  
Author(s):  
Wesley W. O. Souza ◽  
Diorge Brognara ◽  
João A. Leite ◽  
Estevam R. Hruschka Jr.
Keyword(s):  
Machine Learning ◽  
Knowledge Base ◽  
Data Storage ◽  
Language Processing ◽  
Processing Speed ◽  
Question Answering ◽  
Learning Algorithm ◽  
Conversational Agents ◽  
Learning Agent ◽  
The Web

With advances in machine learning, natural language processing, processing speed, and amount of data storage, conversational agents are being used in applications that were not possible to perform within a few years. NELL, a machine learning agent who learns to read the web, today has a considerably large ontology and while it can be used for multiple fact queries, it is also possible to expand it further and specialize its knowledge. One of the first steps to succeed is to refine existing knowledge in NELL’s knowledge base so that future communication between it and humans is as natural as possible. This work describes the results of an experiment where we investigate which machine learning algorithm performs best in the task of classifying candidate words to subcategories in the NELL knowledge base.

Open Issues in Opinion Mining

2019 ◽  
pp. 283-297
Author(s):  
Vishal Vyas ◽  
V. Uma
Keyword(s):  
Social Networking ◽  
Language Processing ◽  
Opinion Mining ◽  
User Generated Content ◽  
Web Content ◽  
Research Issues ◽  
Web Forums ◽  
Stage Process ◽  
Rich Information ◽  
Available Information

Opinions are found everywhere. In web forums like social networking websites, e-commerce sites, etc., rich user-generated content is available in large volume. Web 2.0 has made rich information easily accessible. Manual insight extraction of information from these platforms is a cumbersome task. Deriving insight from such available information is known as opinion mining (OM). Opinion mining is not a single-stage process. Text mining and natural language processing (NLP) is used to obtain information from such data. In NLP, content from the text corpus is pre-processed, opinion word is extracted, and analysis of those words is done to get the opinion. The volume of web content is increasing every day. There is a demand for more ingenious techniques, which remains a challenge in opinion mining. The efficiency of opinion mining systems has not reached the satisfactory level because of the issues in various stages of opinion mining. This chapter will explain the various research issues and challenges present in each stage of opinion mining.

scholarly journals Exploring the Web and Semantic Knowledge-Driven Automatic Question Answering System

2018 ◽  
Vol 7 (3.6) ◽  
pp. 379
Author(s):  
S Jayalakshmi ◽  
Ananthi Sheshaayee
Keyword(s):  
Language Processing ◽  
Question Answering ◽  
Information Source ◽  
Semantic Knowledge ◽  
High Accuracy ◽  
Question Answering System ◽  
Exact Answer ◽  
Ontology Structure ◽  
The Right ◽  
The Web

The growth of information retrieval from the web sources are increased day by day, proving an effective and efficient way to the user for retrieving relevant documents from the web is an art. Asking the right question and retrieving a right answer to the posted query is a service which provide by the Natural Language Processing. Question Answering System is one of the best ways to identify the candidate answer with high accuracy. The web and Semantic Knowledge Driven Question Answering System (QAS) used to determine the candidate answer for the posted query in the NLP tools.  This method includes Query expansion techniques and entity linking method to identify the information source snippets with ontology structure, also ranking the sentences by applying conditional probability between query and Answer to identify the optimal answer from the web corpus. The result provides an exact answer with high accuracy than the baseline method.  

scholarly journals Survey Paper on Opinion Mining - Online Reviews

2019 ◽  
pp. 74-76
Author(s):  
S. Susmitha ◽  
A. Syedrabiya ◽  
Mrs. N. Sathyapriya
Keyword(s):  
Language Processing ◽  
Opinion Mining ◽  
Online Reviews ◽  
Processing Technique ◽  
Entertainment Education ◽  
Survey Paper ◽  
Internet Users ◽  
Almost All ◽  
The Web

Now day’s world is full of Internet, almost all work can be done with the help of it, from simple mobile phone recharge to biggest business process can be done with the help of this technology. People spent their amount of the time surfing on the Web it becomes a new source of entertainment, education, banking, social media, shopping etc. Internet users not only use these websites but also give their opinions and suggestions about internet sources that will be useful for more users who are interested in sites. Like this large amount of opinions and reviews are collected from many users on the Web that needs to be explored, analysed and organized for better decision making. Opinion Mining or Sentiment Analysis, it is widely based on Natural language processing technique and user’s reviews or opinions or suggestions are identified by the information Extraction task. The views reviewed by user explained in the form of positive, negative or natural comments and quotes underlying the text. These reviews are analysed to determine the opinion of the users about the objects. It is impossible to manually analyse those reviews. To overcome the problem, many algorithms are proposed for mining the opinions of the users. Algorithms enable us to extract opinions from the Internet and predict customer's preferences. This paper presents various techniques used for opinion classification by different authors and its accuracy in the classification of opinions.

scholarly journals Opinion Mining and Sentiment Analysis: A Survey

2012 ◽  
Vol 2 (3) ◽  
pp. 171-178 ◽  
Author(s):  
Mohammad Sadegh Hajmohammadi ◽  
Roliana Ibrahim ◽  
Zulaiha Ali Othman
Keyword(s):  
Sentiment Analysis ◽  
Language Processing ◽  
Opinion Mining ◽  
Time Machine ◽  
Web Documents ◽  
Customer Reviews ◽  
The Past ◽  
New Research ◽  
Machine Readable ◽  
Research Domain

In the past few years, a great attention has been received by web documents as a new source of individual opinions and experience. This situation is producing increasing interest in methods for automatically extracting and analyzing individual opinion from web documents such as customer reviews, weblogs and comments on news. This increase was due to the easy accessibility of documents on the web, as well as the fact that all these were already machine-readable on gaining. At the same time, Machine Learning methods in Natural Language Processing (NLP) and Information Retrieval were considerably increased development of practical methods, making these widely available corpora. Recently, many researchers have focused on this area. They are trying to fetch opinion information and analyze it automatically with computers. This new research domain is usually called Opinion Mining and Sentiment Analysis. . Until now, researchers have developed several techniques to the solution of the problem. This paper try to cover some techniques and approaches that be used in this area.

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