Social media analytics: Extracting and visualizing Hilton hotel ratings and reviews from TripAdvisor

Sentiment analysis (SA) has become one of the most active and progressively popular areas in information retrieval and text mining due to the expansion of the World Wide Web (WWW). SA deals with the computational treatment or the classification of user's sentiments, opinions and emotions hidden within the text. Aspect extraction is the most vital and extensively explored phase of SA to carry out the classification of sentiments in precise manners. During the last decade, enormous number of research has focused on identifying and extracting aspects. Therefore, in this survey, a comprehensive overview has been attempted for different aspect extraction techniques and approaches. These techniques have been categorized in accordance with the adopted approach. Despite being a traditional survey, a comprehensive comparative analysis is conducted among different approaches of aspect extraction, which not only elaborates the performance of any technique but also guides the reader to compare the accuracy with other state-of-the-art and most recent approaches.

Aspect extraction in sentiment analysis: comparative analysis and survey

Sentiment analysis of scientific citations has received much attention in recent years because of the increased availability of scientific publications. Scholarly databases are valuable sources for publications and citation information where researchers can publish their ideas and results. Sentiment analysis of scientific citations aims to analyze the authors’ sentiments within scientific citations. During the last decade, some review papers have been published in the field of sentiment analysis. Despite the growth in the size of scholarly databases and researchers’ interests, no one as far as we know has carried out an in-depth survey in a specific area of sentiment analysis in scientific citations. This paper presents a comprehensive survey of sentiment analysis of scientific citations. In this review, the process of scientific citation sentiment analysis is introduced and recently proposed methods with the main challenges are presented, analyzed and discussed. Further, we present related fields such as citation function classification and citation recommendation that have recently gained enormous attention. Our contributions include identifying the most important challenges as well as the analysis and classification of recent methods used in scientific citation sentiment analysis. Moreover, it presents the normal process, and this includes citation context extraction, public data sources, and feature selection. We found that most of the papers use classical machine learning methods. However, due to limitations of performance and manual feature selection in machine learning, we believe that in the future hybrid and deep learning methods can possibly handle the problems of scientific citation sentiment analysis more efficiently and reliably.

A survey on sentiment analysis of scientific citations

Explicit aspects extraction in sentiment analysis using optimal rules combination

Other people’s opinions are important piece of information for making informed decisions. Today the Web has become an excellent source of consumer opinions. However, as the volume of opinionated text is growing rapidly, it is getting impossible for users to read all reviews to make a good decision. Reading different and possibly even contradictory opinions written by different reviewers even make them more confused. In the same way, monitoring consumer opinions is getting harder for the manufactures and providers. These needs have inspired a new line of research on mining consumer reviews, or opinion mining. Aspect-based opinion mining, is a relatively new sub-problem that attracted a great deal of attention in the last few years. Extracted aspects and estimated ratings clearly provides more detailed information for users to make decisions and for suppliers to monitor their consumers. In this thesis, we address the problem of aspect-based opinion mining and seek novel methods to improve limitations and weaknesses of current techniques. We first propose a method, called Opinion Digger, that takes advantages of syntactic patterns to improve the accuracy of frequencybased techniques. We then move on to model-based approaches and propose an LDA-based model, called ILDA, to jointly extract aspects and estimate their ratings. In our next work, we compare ILDA with a series of increasingly sophisticated LDA models representing the essence of the major published methods in the literature. A comprehensive evaluation of these models indicates that while ILDA works best for items with large number of reviews, it performs poorly when the size of the training dataset is small, i.e., for cold start items. The cold start problem is critical as in real-life data sets around 90% of items are cold start. We address this problem in our last work and propose an LDA-based model, called FLDA. It models items and reviewers by a set of latent factors and learns them using reviews of an item category. Experimental results on real life data sets show that FLDA achieve significant gain for cold start items compared to the state-of-the-art models.

https://summit.sfu.ca/system/files/iritems1/12790/etd7688_SAbbasiMoghaddam.pdf

Aspect-based opinion mining in online reviews

Opinion mining is a challenging task to identify the opinions or sentiments underlying user generated contents, such as online product reviews, blogs, discussion forums, etc. Previous studies that adopt machine learning algorithms mainly focus on designing effective features for this complex task. This paper presents our approach based on tree kernels for opinion mining of online product reviews. Tree kernels alleviate the complexity of feature selection and generate effective features to satisfy the special requirements in opinion mining. In this paper, we define several tree kernels for sentiment expression extraction and sentiment classification, which are subtasks of opinion mining. Our proposed tree kernels encode not only syntactic structure information, but also sentiment related information, such as sentiment boundary and sentiment polarity, which are important features to opinion mining. Experimental results on a benchmark data set indicate that tree kernels can significantly improve the performance of both sentiment expression extraction and sentiment classification. Besides, a linear combination of our proposed tree kernels and traditional feature vector kernel achieves the best performances using the benchmark data set.

An Approach Based on Tree Kernels for Opinion Mining of Online Product Reviews

: This Paper presents the work done for the TREC 2010 entity track. We concentrate on constructing enriched anchor text model by exploiting hierarchical information presented in web pages to retrieve promising pages, and heuristic rules to extract potential candidate entities by zooming in the right section.

/pdf/reconstruct-logical-hierarchical-sitemap-for-related-entity-5d2uadggsu.pdf

Reconstruct Logical Hierarchical Sitemap for Related Entity Finding

: Our goal in participating in the TREC 2009 Entity Track is to study whether QA list technique can help improve accuracy of the entity finding task. Also, we take a looking for homepage finding to identify homepages of an entity by training a maximum entropy classifier and a logistic regression models for three types of entity respectively.

/pdf/experiments-on-related-entity-finding-track-at-trec-2009-2dtpfmnokj.pdf

Experiments on Related Entity Finding Track at TREC 2009

: This Paper presents the work done for the TREC 2009 faceted blog distillation task of blog track. In our approach, we use a mixture of language models based on global representation. Our model can be regarded as a combination of topic relevance model and faceted relevance model. By pseudorelevance feedback method, we can estimate the above two models from topic relevance feedback documents and facet relevance feedback documents respectively. Experimental results on TREC blogs08 collection show the effectiveness of our proposed approach.

/pdf/bit-at-trec-2009-faceted-blog-distillation-task-2s6loxutvv.pdf

BIT at TREC 2009 Faceted Blog Distillation Task

Temporal information processing plays an important role in many application areas such as information retrieval, question answering, machine translation, and text summarization This paper proposes a transformation-based error-driven learning approach to extracting temporal expressions from Chinese unstructured texts The temporal expression annotator used in the approach is developed based on a Chinese time ontology, which includes concepts of temporal expressions and their taxonomical relations Experiments in three domains show that our algorithm obtained promising results

A transformation-based error-driven learning approach for Chinese temporal information extraction

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.

Qing Yang

Author Tools

Chat about Author