Sentiment analysis and opinion mining is the field of study that analyzes people's opinions, sentiments, evaluations, attitudes, and emotions from written language. It is one of the most active research areas in natural language processing and is also widely studied in data mining, Web mining, and text mining. In fact, this research has spread outside of computer science to the management sciences and social sciences due to its importance to business and society as a whole. 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. For the first time in human history, we now have a huge volume of opinionated data recorded in digital form for analysis. Sentiment analysis systems are being applied in almost every business and social domain because opinions are central to almost all human activities and are key influencers of our behaviors. Our beliefs and perceptions of reality, and the choices we make, are largely conditioned on how others see and evaluate the world. For this reason, when we need to make a decision we often seek out the opinions of others. This is true not only for individuals but also for organizations. This book is a comprehensive introductory and survey text. It covers all important topics and the latest developments in the field with over 400 references. It is suitable for students, researchers and practitioners who are interested in social media analysis in general and sentiment analysis in particular. Lecturers can readily use it in class for courses on natural language processing, social media analysis, text mining, and data mining. Lecture slides are also available online.

/pdf/sentiment-analysis-and-opinion-mining-8314pykwrf.pdf

Sentiment Analysis and Opinion Mining

The increasing popularity of Web 20 has led to exponential growth of user-generated content in both volume and significance One important type of user-generated content is the blog Blogs encompass useful information (eg, insightful product reviews and information-rich consumer communities) that could potentially be a gold mine for business intelligence, bringing great opportunities for both academic research and business applications However, performing business intelligence on blogs is quite challenging because of the vast amount of information and the lack of commonly adopted methodology for effectively collecting and analyzing such information In this paper, we propose a framework for gathering business intelligence from blogs by automatically collecting and analyzing blog contents and bloggers' interaction networks Through a system developed using the framework, we conducted two case studies with one case focusing on a consumer product and the other on a company Our case studies demonstrate how to use the framework and appropriate techniques to effectively collect, extract, and analyze blogs related to the topics of interest, reveal novel patterns in the blogger interactions and communities, and answer important business intelligence questions in the domains The framework is sufficiently generic and can be applied to any topics of interest, organizations, and products Future academic research and business applications related to the topics examined in the two cases can also be built using the findings of this study

/pdf/business-intelligence-in-blogs-understanding-consumer-3iq95ntcov.pdf

Business intelligence in blogs: understanding consumer interactions and communities

Multiword expressions MWEs are a class of linguistic forms spanning conventional word boundaries that are both idiosyncratic and pervasive across different languages. The structure of linguistic processing that depends on the clear distinction between words and phrases has to be re-thought to accommodate MWEs. The issue of MWE handling is crucial for NLP applications, where it raises a number of challenges. The emergence of solutions in the absence of guiding principles motivates this survey, whose aim is not only to provide a focused review of MWE processing, but also to clarify the nature of interactions between MWE processing and downstream applications. We propose a conceptual framework within which challenges and research contributions can be positioned. It offers a shared understanding of what is meant by "MWE processing," distinguishing the subtasks of MWE discovery and identification. It also elucidates the interactions between MWE processing and two use cases: Parsing and machine translation. Many of the approaches in the literature can be differentiated according to how MWE processing is timed with respect to underlying use cases. We discuss how such orchestration choices affect the scope of MWE-aware systems. For each of the two MWE processing subtasks and for each of the two use cases, we conclude on open issues and research perspectives.

/pdf/multiword-expression-processing-a-survey-4eaqwifn7v.pdf

Multiword expression processing: A survey

The rapid growth of online social media in the form of collaborativelycreated content presents new opportunities and challenges to both producers and consumers of information. With the large amount of data produced by various social media services, text analytics provides an effective way to meet usres’ diverse information needs. In this chapter, we first introduce the background of traditional text analytics and the distinct aspects of textual data in social media. We next discuss the research progress of applying text analytics in social media from different perspectives, and show how to improve existing approaches to text representation in social media, using real-world examples.

Text Analytics in Social Media

Methods, computer program products and systems are described for converting speech to text. Sound information is received at a computer server system from an electronic device, where the sound information is from a user of the electronic device. A context identifier indicates a context within which the user provided the sound information. The context identifier is used to select, from among multiple language models, a language model appropriate for the context. Speech in the sound information is converted to text using the selected language model. The text is provided for use by the electronic device.

Language Model Selection for Speech-to-Text Conversion

This paper describes our participation in the TREC 2008 Blog Track. For the opinion task, we made an opinion retrieval model that consists of preprocessing, topic retrieval, opinion finding, and sentiment classification parts. For topic retrieval, our system is based on the passage-based retrieval model and feedback. For the opinion analysis, we created a pseudo opinionated word (POW), O, which is representative of all opinion words, and expanded the original query with O. For the blog distillation task, we integrated the average score of all posts within a feed, and the average score of the most relevant N post scores. We also examined the pseudorelevance feedback for the distillation task by focusing on various document selection schemes to expand the query terms. The experimental results show a significant improvement over previous results.

/pdf/kle-at-trec-2008-blog-track-blog-post-and-feed-retrieval-3skfk50eg6.pdf

KLE at TREC 2008 Blog Track: Blog Post and Feed Retrieval

Lexicon-based approaches have been widely used for opinion retrieval due to their simplicity. However, no previous work has focused on the domain-dependency problem in opinion lexicon construction. This paper proposes simple feedback-style learning for query-specific opinion lexicon using the set of top-retrieved documents in response to a query. The proposed learning starts from the initial domain-independent general lexicon and creates a query-specific lexicon by re-updating the opinion probability of the initial lexicon based on top-retrieved documents. Experimental results on recent TREC test sets show that the query-specific lexicon provides a significant improvement over previous approaches, especially in BLOG-06 topics.

Improving Opinion Retrieval Based on Query-Specific Sentiment Lexicon

The analysis of query logs from blog search engines show that news-related queries occupy a significant portion of the logs. This raises a interesting research question on whether the blogosphere can be used to identify important news stories. In this paper, we present novel approaches to identify important news story headlines from the blogosphere for a given day. The proposed system consists of two components based on the language model framework, the query likelihood and the news headline prior. For the query likelihood, we propose several approaches to estimate the query language model and the news headline language model. We also suggest several criteria to evaluate the news headline prior that is the prior belief about the importance or newsworthiness of the news headline for a given day. Experimental results show that our system significantly outperforms a baseline system. Specifically, the proposed approach gives 2.62% and 10.19% further increases in MAP and P@5 over the best performing result of the TREC'09 Top Stories Identification Task.

Mining the blogosphere for top news stories identification

One of the important issues in blog search engines is to extract the cleaned text from blog post. In practice, this extraction process is confronted with many non-relevant contents in the original blog post, such as menu, banner, site description, etc, causing the ranking be less-effective. The problem is that these non-relevant contents are not encoded in a unified way but encoded in many different ways between blog sites. Thus, the commercial vendor of blog sites should consider tuning works such as making human-driven rules for eliminating these non-relevant contents for all blog sites. However, such tuning is a very inefficient process. Rather than this labor-intensive method, this paper first recognizes that many of these non-relevant contents are not changed between several consequent blog posts, and then proposes a simple and effective DiffPost algorithm to eliminate them based on content difference between two consequent blog posts in the same blog site. Experimental result in TREC blog track is remarkable, showing that the retrieval system using DiffPost makes an important performance improvement of about 10% MAP (Mean Average Precision) increase over that without DiffPost.

DiffPost: Filtering Non-relevant Content Based on Content Difference between Two Consecutive Blog Posts

Search results clustering helps users to browse the search results and locate what they are looking for. In the search result clustering, the label selection which annotates a meaningful phrase for each cluster becomes the most fundamental issue. In this paper, we present a new method of using the language modeling approach over Dmoz for label selection, namely label language model. Experimental results show that our method is helpful to obtain meaningful clustering labels of search results.

Search Result Clustering Using Label Language Model

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Yeha Lee

Author Tools

Chat about Author