A systematic literature review on opinion types and sentiment analysis techniques: Tasks and challenges

 

Abstract

Purpose: 

The purpose of this paper is to map the evidence provided on the review types, and explain the challenges faced by classification techniques in sentiment analysis (SA). The aim is to understand how traditional classification technique issues can be addressed through the adoption of improved methods. 

Design/methodology/approach: 

A systematic review of literature was used to search published articles between 2002 and 2014 and identified 24 papers that discuss regular, comparative, and suggestive reviews and the related SA techniques. The authors formulated and applied specific inclusion and exclusion criteria in two distinct rounds to determine the most relevant studies for the research goal. 

Findings: 

The review identified nine practices of review types, eight standard machine learning classification techniques and seven practices of concept learning Sentic computing techniques. This paper offers insights on promising concept-based approaches to SA, which leverage commonsense knowledge and linguistics for tasks such as polarity detection. The practical implications are also explained in this review. 

Research limitations/implications: 

The findings provide information for researchers and traders to consider in relation to a variety of techniques for SA such as Sentic computing and multiple opinion types such as suggestive opinions. 

Originality/value: 

Previous literature review studies in the field of SA have used simple literature review to find the tasks and challenges in the field. In this study, a systematic literature review is conducted to find the more specific answers to the proposed research questions. This type of study has not been conducted in the field previously and so provides a novel contribution. Systematic reviews help to reduce implicit researcher bias. Through adoption of broad search strategies, predefined search strings and uniform inclusion and exclusion criteria, systematic reviews effectively force researchers to search for studies beyond their own subject areas and networks.

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https://www.emerald.com/insight/content/doi/10.1108/IntR-04-2016-0086/full/html

Why Valerie Solana Shot Andy Warhol

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On This Day: Valerie Solanas Shoots Andy Warhol

On June 3, 1968, radical feminist Valerie Solanas shot pop culture artist Andy Warhol in his Manhattan studio. Though he was initially being pronounced dead, Warhol survived.
Andy Warhol Nearly Killed

Valerie Solanas, angered that Andy Warhol had lost a script of hers, made an afternoon visit to Warhol’s Midtown Manhattan studio, known as The Factory, with a .32 revolver stashed in a brown paper bag. Warhol, accompanied by his boyfriend, Jed Johnson, and art critic Mario Amaya, saw Solanas outside The Factory and invited her in.

Once inside, Solanas pulled out the gun. The Village Voice’s Howard Smith described: “Warhol turned and saw the gun. ‘Valerie,’ he yelled. ‘Don't do it! No! No!’ She fired three shots, and Warhol fell to the floor.”

Solanas then shot Amaya and aimed at Warhol’s manager, Fred Hughes, but her gun jammed and she fled. She later turned herself in to rookie traffic cop William Shemalix, handing him her gun and saying she had shot Andy Warhol “because he had too much control of my life.”

After the shooting, Warhol was taken to Columbus Hospital and was pronounced dead, but doctors resuscitated him and he survived after emergency surgery.

Source: http://www.findingdulcinea.com/news/on-this-day/May-June-08/On-this-Day--Valerie-Solanas-Shoots-Andy-Warhol.html
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Approaches to Cross-Domain Sentiment Analysis: A Systematic Literature Review

 

Abstract

A sentiment analysis has received a lot of attention from researchers working in the fields of natural language processing and text mining. 

However, there is a lack of annotated data sets that can be used to train a model for all domains, which is hampering the accuracy of sentiment analysis. 

Many research studies have attempted to tackle this issue and to improve cross-domain sentiment classification. 

In this paper, we present the results of a comprehensive systematic literature review of the methods and techniques employed in a cross-domain sentiment analysis. 

We focus on studies published during the period of 2010-2016. 

From our analysis of those works, it is clear that there is no perfect solution. 

Hence, one of the aims of this review is to create a resource in the form of an overview of the techniques, methods, and approaches that have been used to attempt to solve the problem of cross-domain sentiment analysis in order to assist researchers in developing new and more accurate techniques in the future.

https://ieeexplore.ieee.org/abstract/document/7891035

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The Effects of Emoji in Sentiment Analysis

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Abstract: This study investigates the usage of Emoji characters on social networks and the effects of Emoji in  text  mining and sentiment  analysis. As  it provides live access  to  text  based  public opinions,  we  chose Twitter  as our  information  source  in  our  analysis.  We  collected  text  data  for  some  global  positive  and negative events to analyze the impact of Emoji characters in sentiment analysis. In our analysis, we noticed that  the  utilization  of  Emoji  characters  in  sentiment  analysis  results  in  higher  sentiment  scores. Furthermore, we observed that the usage of Emoji characters in sentiment analysis appeared to have higher impact on overall sentiments of the positive opinions in comparison to the negative opinions.    Key words: Emoji, opinion mining, sentiment analysis, twitter.
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https://www.researchgate.net/publication/320446679_The_Effects_of_Emoji_in_Sentiment_Analysis

A Framework and practical implementation for sentiment analysis and aspect exploration [problem statement]

 

.

1.2 Problem statement and research questions

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The explosion of the Web 2.0 has not only brought us a huge volume of opinionated data recorded in digital forms, but also provided us a great opportunity to understand the sentiment of the public by analysing these large-scale data. However, all of the user generated data is a double-edged sword: the larger the size of the data, the more difficult it is to extract useful information. A survey shows that Facebook generates 250 million posts per hour and Twitter users on the other hand generate 21 million tweets per hour (George, 2015). Nowadays, the review website TripAdvisor 4 generates more than 255 reviews every minute and nearly 2,600 new topics are posted every day. So far TripAdvisor has over 385 million reviews and opinions from users around the world (TripAdvisor, 2016). Facing such big data, studies have already revealed that more than half of online customers encounter frustrations during their online shopping. It makes difficult for a potential customer to read the reviews and make an informed decision. Around 30% of online customers have felt confused and overwhelmed by the amount of information, since there is a large number of spam or duplicate content in websites (Horrigan, 2008; Niven, 2012).

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Although we are in the era of Web 2.0, flooded with tons of data every day, companies and organizations also face problems dealing with the opinionated data effectively. A survey shows that three quarters of 2,100 organizations do not have a clear idea of what their most valuable customers think about them and nearly 31% of them find it difficult to measure customers’ opinions (Michael, 2012). It is obvious that they do not lack the data sources of customers’ opinions, but the overwhelming size of opinionated data and the complexity of dealing with subjectivity, makes it difficult to extract useful information for organizations. 

.

The need to deal with these unstructured opinionated data naturally leads to the rise of research in the field of sentiment analysis. Sentiment analysis has been one of the most active research areas in natural language processing (NLP) since 2002 (see Section 2.3). The main task of sentiment analysis is to automatically determine the semantic orientation (SO) in a given document (Turney, 2002; Pang and Lee, 2008;). Semantic orientation (SO) refers to a measure of opinions and subjectivity, which indicates the polarity (positive, negative or neutral) and strength of words, phrases, sentences or documents (Hatzivassiloglou and McKeown, 1997; Turney 2002; Liu, 2010). Currently research on sentiment analysis has been dominated by two basic approaches: the first one is machine learning approach, which aims to build text classifiers by selecting right text features and algorithms from labelled instances of texts (see Section 2.5.2). The other is semantic orientation approach, which involves calculating the overall polarity via the semantic orientation of words or phrases in the text (see Section 2.5.1). Since the latter approach utilizes lexical resources like lists of opinion-bearing words, lexicons, dictionaries etc., it is also referred as lexicon-based approach (Peng and Park, 2004; Ding et al., 2008; Na et al., 2009; Taboada et al., 2011; Molina-González et al., 2015). Thus in this thesis, the terms ‘semantic orientation approach’ and ‘lexicon-based approach’ are used interchangeably.

.

Many sentiment analysis tools and applications have been developed to mine the opinions in user generated content in the Web. However, the performances are very poor due to the complexity of natural language (Sobkowicz et al. 2012, Mohammad et al., 2013; Maynard, 2016). In essence, sentiment analysis is still a problem of natural language processing (NLP), which deals with the natural language documents, which are also called unstructured data (Liu, 2012). Prior researches show that sentiment analysis is more difficult than the traditional topic-based text classification (Pang and Lee, 2008). Although various approaches have been proposed to conduct sentiment analysis, it is still difficult to deal with some linguistic phenomena, such as negation and mix-opinion text. This leads to low accuracy of sentiment classification (Vinodhini, and Chandrasekaran, 2012; Park et al., 2015; Khan et al., 2016). Besides, it is insufficient to only determine the polarity of the opinions, since an opinion without a target is of limited use. The task of extracting the opinions and their targets simultaneously, is also called aspect-level sentiment analysis in the research literature and is more difficult to achieve (Liu, 2012). Current studies show that the methods dealing with aspect-level sentiment analysis are limited (see Section 2.3.3). 

.

Due to the existing real-world problems in dealing with the big data and current research gaps (see Section 2.4.4 for more details), the research presented in this thesis is motivated to address the following two research questions: 

1) How can online product reviews be automatically and accurately classified with respect to their sentiments?

2) How to detect the aspects of sentiments shown in the online product reviews effectively? 

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The first research question concerns the need to manage the large amount of online reviews automatically and improve the performance of sentiment classification. The second research question underlines the significance to identify the targets of the opinions, which pursues to help individuals to make an informed purchasing decision and provide manufacturers insight in order improve their products or services. 

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1.3 Aim and objectives 

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The aim of this thesis is to explore an effective way to conduct fine-grained sentiment analysis by improving the performance of sentiment classification and extracting aspects related with the sentiments. To cater for this aim, there are three objectives that this research has tried to achieve.

.

The first objective intends to handle the text that contains positive and negative orientated opinions, because most of the real-word data shows that positive and negative sentiments co-occur in the same document. Most documents will have both positive and negative views. Besides the aspects (attributes of an entity that a review is about) of the opinions can be various, and therefore, it is essential to separate the mixed-opinion reviews.

.

Secondly, following the semantic orientation approach for sentiment analysis (see Section 2.5.1), a domain sentiment lexicon needs to be constructed and is used to determine the polarity of a document. The sentiment lexicon contains the words with their sentiment inclinations. Due to various domains, words could be used differently and show opposite sentiment orientations in each domain. Thus the sentiment lexicon used for sentiment analysis is the key to obtaining more accurate results.

.

Furthermore, online product reviews include a variety of aspects (see Section 2.3.3). Therefore, the third objective is to extract the aspects of the products within a review, instead of predefining them, and then identify the sentiments about them.

.

Achieving these three objectives should lead to a coherent sentiment analysis framework that is proposed in this research (see Chapter 3), which aims to improve the performance of sentiment classification and provide in-depth aspect-level analysis. 

.

https://www.research.manchester.ac.uk/portal/files/55559300/FULL_TEXT.PDF

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Making Sense of Pattern Grading

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One pattern, three sizes
	A base size 12 pattern (left) can be graded up to a size 16 (center) using the cut-and-spread method, and similarly graded down to a size 6 (right)  by cutting and overlapping along specified cut lines.

Methods of gradingThere are three basic methods of grading: cut and spread, pattern shifting, and computer grading. No one method is technically superior and all are equally capable of producing a correct grade.

Cut-and-spread method: The easiest method, which is the basis of the other two methods, is to cut the pattern and spread the pieces by a specific amount to grade up, or overlap them to grade down. No special training or tools are required-just scissors, a pencil, tape, and a ruler that breaks 1 in. down to 1/64.

Pattern shifting: Pattern shifting is the process of increasing the overall dimensions of a pattern by moving it a measured distance up and down and left and right, (using a specially designed ruler) and redrawing the outline, to produce the same results as the cut-and-spread method.

The most recent development, computer grading, is the fastest method, but tends to be an investment only larger manufacturers can afford. However, sophisticated home computer software is becoming affordable.

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woodward "epidemiology: study design and data analysis"

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