An Improved Method for Extractive Based Opinion Summarization Using Opinion Mining

Opinion mining and summarization of the increasing user-generated content on different digital platforms (e.g., news platforms) are playing significant roles in the success of government programs and initiatives in digital governance, from extracting and analyzing citizen’s sentiments for decision-making. Opinion mining provides the sentiment from contents, whereas summarization aims to condense the most relevant information. However, most of the reported opinion summarization methods are conceived to obtain generic summaries, and the context that originates the opinions (e.g., the news) has not usually been considered. In this paper, we present a context-aware opinion summarization model for monitoring the generated opinions from news. In this approach, the topic modeling and the news content are combined to determine the “importance” of opinionated sentences. The effectiveness of different developed settings of our model was evaluated through several experiments carried out over Spanish news and opinions collected from a real news platform. The obtained results show that our model can generate opinion summaries focused on essential aspects of the news, as well as cover the main topics in the opinionated texts well. The integration of term clustering, word embeddings, and the similarity-based sentence-to-news scoring turned out the more promising and effective setting of our model.

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Opinion Mining and Sentiment Analysis

The Oxford Handbook of Computational Linguistics 2nd edition ◽

10.1093/oxfordhb/9780199573691.013.43 ◽

2017 ◽

Author(s):

Eric Breck ◽

Claire Cardie

Keyword(s):

Sentiment Analysis ◽

Question Answering ◽

Opinion Mining ◽

Sports Fans ◽

Fine Grained ◽

Opinion Analysis ◽

Opinion Summarization ◽

Automatic Methods ◽

Analysis System ◽

Compositional Approaches

Opinions are ubiquitous in text, and readers of online text—from consumers to sports fans to news addicts to governments—can benefit from automatic methods that synthesize useful opinion-oriented information from the sea of data. In this chapter on opinion mining and sentiment analysis, we introduce an idealized, end-to-end opinion analysis system and describe its components. We present methods for classifying documents and text passages according to their sentiment as well as methods that perform more fine-grained extraction of opinion expressions, their holders and their targets. We also address supplementary tasks of opinion lexicon construction, opinion summarization, opinion-oriented question answering, multi-lingual sentiment analysis and compositional approaches to phrase-level sentiment analysis.

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Evaluation of Data Mining Features, Features Taxonomies and their Applications

Kurdistan Journal of Applied Research ◽

10.24017/science.2017.3.3 ◽

2017 ◽

Vol 2 (3) ◽

pp. 131-141

Author(s):

Shirin Noekhah ◽

Naomie Binti Salim ◽

Nor Hawaniah Zakaria

Keyword(s):

Data Mining ◽

Recommendation System ◽

Opinion Mining ◽

Extensive Study ◽

Comprehensive Investigation ◽

Fundamental Domains ◽

Opinion Summarization ◽

Opinion Spam ◽

Evaluation Of Data ◽

Potential Customers

The World Wide Web has brought an enormous improvement in the lives of people, during the last couple of decades. E-commerce is a new area arisen during this evolutionary period and has changed the traditional trading approaches for selling products and services. It uses different techniques to discover a market trend and analyze the competitor’s activities by exploiting reviews’ information. On the other hand, potential customers, also, use the online opinion to make their purchase decision. Opinion mining and sentiment analysis are the most critical and fundamental domains of data mining which can be useful for variety its sub-domains such as opinion summarization, recommendation system and opinion spam detection. Opinion mining and all its sub-branches can be performed efficiently when there is a comprehensive understanding of the most effective features applied in those domains. To achieve the best results, we need to use the most proper set of features for different case studies in order to classification or clustering. To the best of our knowledge, there is no extensive study and taxonomy of variety range of features and their applications in opinion mining. In this paper, we do comprehensive investigation on various types of features exploited in variety sub-branches of opinion mining domain. We present the most frequent features’ sets including structural, linguistic and relation-based features as a complete reference for further opinion mining research. The results proved that using multiple types of features improve the accuracy of opinion mining applications.

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Sentiment Analysis and Opinion Summarization of Product Feedback

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.b1011.0782s419 ◽

2019 ◽

Vol 8 (2S4) ◽

pp. 59-64

Keyword(s):

Artificial Intelligence ◽

Sentiment Analysis ◽

Language Processing ◽

Opinion Mining ◽

Online Shopping ◽

User Interaction ◽

Decision Making Process ◽

Rule Based ◽

Opinion Summarization ◽

Rule Based Approach

With the exponential growth of online shopping platforms, user interaction is made direct through their reviews and ratings. User’s opinions and experiences are a significant source of valuable information in decision making process. In recent days, almost every website encourages users to express and exchange their views, suggestions and opinions related to product, services, policies, etc. publicly. Opinion mining is an extensive branch of Artificial Intelligence and a form of Natural Language Processing which illustrates the attitude of the customers, in specific services or products. Also known as Sentiment Analysis, it aims at determining the response and mood or attitude of the speaker or the overall contextual and emotional polarity or reaction. Existing algorithms determine sentiment by training on datasets, lexicon-based approach by calculating polarity and rule-based approach for classification. Opinion Summarization is the process of consolidating a large amount of sentiments and opinions into a clear and brief statement for an easier grasp on the underlying context. Major summarization methods include, Extractive method, Sentence Ranking, Abstractive method and Clustering of Textual Segments. Hence it is important to judge and classify these reviews and present a laconic opinion so it would be easier for users to obtain a gist and overall polarity on the various reviews instead of going through all of them

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An Improved Method for the Preparation and TEM Examination of Sharp Pointed Tips used in APFIM and STM

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100134107 ◽

1990 ◽

Vol 48 (2) ◽

pp. 102-103

Author(s):

E.A. Fischione ◽

P.E. Fischione ◽

J.J. Haugh ◽

M.G. Burke

Keyword(s):

Atom Probe ◽

Preparation Process ◽

Improved Method ◽

Ion Milling ◽

Polishing Process ◽

Probe Field ◽

Scanning Tunnelling ◽

Stm Tips ◽

Tunnelling Microscopy ◽

Ion Microscopy

A common requirement for both Atom Probe Field-Ion Microscopy (APFIM) and Scanning Tunnelling Microscopy (STM) is a sharp pointed tip for use as either the specimen (APFIM) or the probe (STM). Traditionally, tips have been prepared by either chemical or electropolishing techniques. Recently, ion-milling has been successfully employed in the production of APFIM tips [1]. Conventional electropolishing techniques are applicable to a wide variety of metals, but generally require careful manual adjustments during the polishing process and may also be time-consuming. In order to reduce the time and effort involved in the preparation process, a compact, self-contained polishing unit has been developed. This system is based upon the conventional two-stage electropolishing technique in which the specimen/tip blank is first locally thinned or “necked”, and subsequently electropolished until separation occurs.[2,3] The result of this process is the production of two APFIM or STM tips. A mechanized polishing unit that provides these functions while automatically maintaining alignment has been designed and developed.

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Immunoelectron microscopic localization of elastic tissue in archival material using an improved method

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100161539 ◽

1990 ◽

Vol 48 (3) ◽

pp. 794-795

Author(s):

J. C. Fanning ◽

J. F. White ◽

R. Polewski ◽

E. G. Cleary

Keyword(s):

Normal Animal ◽

Animal Tissue ◽

Elastic Tissue ◽

Human Tissues ◽

Improved Method ◽

Tissue Samples ◽

Archival Material ◽

Immuno Electron Microscopy ◽

Arteries And Veins ◽

Biochemical Examination

Elastic tissue is an important component of the walls of arteries and veins, of skin, of the lungs and in lesser amounts, of many other tissues. It is responsible for the rubber-like properties of the arteries and for the normal texture of young skin. It undergoes changes in a number of important diseases such as atherosclerosis and emphysema and on exposure of skin to sunlight.We have recently described methods for the localizationof elastic tissue components in normal animal and human tissues. In the study of developing and diseased tissues it is often not possible to obtain samples which have been optimally prepared for immuno-electron microscopy. Sometimes there is also a need to examine retrospectively samples collected some years previously. We have therefore developed modifications to our published methods to allow examination of human and animal tissue samples obtained at surgery or during post mortem which have subsequently been: 1. stored frozen at -35° or -70°C for biochemical examination; 2.

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