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Poster Paper Proc. of Int. Conf. on Advances in Information Technology and Mobile Communication 2013

Design of Automated Sentiment or Opinion Discovery System to Enhance Its Performance M.A.Jawale1, Dr.D.N.Kyatanavar2, and A.B.Pawar3 1

S.R.E.S.College of Engineering, Kopargaon, Maharashtra & JJTU, Rajasthan, India Email: jawale.madhu@gmail.com 2, 3 S.R.E.S.College of Engineering, Kopargaon, Maharashtra & JJTU, Rajasthan, India Email: {kyatanavar, anil.pawar1983}@gmail.com Abstract— In today’s social networking era, if one has to make decision about any product, service or individual performance, the availability of various comments, suggestions, ratings, and feedbacks are abundant. The required decision support data can be collected through different sources of Medias like newspapers, blogs, and discussion forums and from internet too. So surely, it leads to the selection of best product, service or individual if it is analyzed efficiently. In leading and competitive world, this is huge and practical need of industries, organizations to empower their qualities. In the recent years, the significant study is done in the field of sentiment analysis. However, the earlier work focused the implementation and evaluation of individual sub technique of sentiment analysis. Though these implementations produces significant results of sentiment or opinion analysis, the trust of decision makers is still in dangling to accept the results of such analysis. In this paper, initially, we have been described the brief review about the sentiment or opinion analysis system. Then the details are provided about the design and about how to build an automated opinion discovery system to enhance performance of sentiment or opinion analysis based on feature extraction sentiment analysis sub technique, natural language processing and data mining techniques in an integrated way.

products on commercial websites and express their views on almost anything in discussion forums and blogs, and on social network sites. Now, if one wants to buy a product, one is no longer limited to asking one’s friends and families because there are many user reviews on the websites. For a company, it may no longer need to conduct surveys or focus groups in order to gather consumer opinions about its products and those of its competitors because there is a plenty of such information publicly available. However, finding opinion sites and monitoring them on the internet can still be a formidable task because there are large numbers of diverse sites, and each site may also have a huge volume of opinionated text. In many cases, opinions are hidden in long forum posts and blogs [3]. It is difficult for a human reader to find relevant sites, extract related sentences with opinions, read them, summarize them, and organize them into usable forms [20]. Thus, automated opinion discovery and summarization systems are needed. Sentiment analysis is not a single task, but it is a multifaceted problem [3] containing many sub-problems such as object identification, feature extraction and synonym grouping, opinion orientation classification and integration. Survey reported in [5] covers techniques and approaches that promise to directly enable opinion-oriented informationseeking systems. Here, focus is on methods that seek to address the new challenges raised by sentiment-aware applications, as compared to those that are already present in more traditional fact-based analysis and given requirement about building an integrated system that tries to deal with all the multi-faceted problems altogether.

Index Terms— Feature Extraction, Opinion Mining, Part-ofSpeech, Sentiment Analysis, Subjective Classification.

I. INTRODUCTION Sentiment analysis or opinion mining is the computational study of opinions, appraisals, and emotions toward entities, events and their attributes. In the past few years, it has attracted a great deal of attentions from both academia and industry due to many challenging research problems and a wide range of applications[2].Opinions are important because whenever we need to make a decision we want to hear others’ opinions. This is not only true for individuals but also true for organizations. However, there was almost no computational study on opinions before the invention of web technologies because there was little opinionated text available. In the past, when an individual needed to make a decision, he/she was required to ask for opinions from friends and families. When an organization wanted to find opinions of the general public about its products and services, it used to conduct surveys and focus groups. However, with the explosive growth of the social media contents on the websites in the recent past, the world has been transformed. People can now post reviews of © 2013 ACEEE DOI: 03.LSCS.2013.2.79

II. RELATED WORK The research in the field of sentiment analysis was started with sentiment and subjectivity classification, which treated the problem as a text classification problem. Reference [5] covered that traditionally; text categorization seeks to classify documents by topic. There can be many possible categories, the definitions of which might be user- and application dependent; and for a given task, we might be dealing with as few as two classes (binary classification) or as many as thousands of classes (e.g., classifying documents with respect to a complex taxonomy). In contrast, with sentiment classification often have relatively few classes (e.g., “positive” or “3 stars”) that generalize across many 48

Poster Paper Proc. of Int. Conf. on Advances in Information Technology and Mobile Communication 2013 domains and users. In addition, while the different classes in topic-based categorization can be completely unrelated, the sentiment labels that are widely considered in previous work typically represent opposing (if the task is binary classification) or ordinal/numerical categories (if classification is according to a multi-point scale). Sentiment classification identifies whether an opinionated document (e.g., product reviews) or sentence expresses a positive or negative opinion. Subjectivity classification determines whether a sentence is subjective or objective [12]. It states that many real-life applications, however, require more detailed analysis because the user often wants to know what the opinions have been expressed on [2]. For example, from the review of a product, one wants to know what features of the product have been praised and criticized by consumers. Let us use the following review segment on iPhone as an example to introduce the general problem (a number is associated with each sentence for easy reference): “(1) I bought an iPhone 2 days ago. (2) It was such a nice phone. (3) The touch screen was really cool. (4) The voice quality was clear too. (5) However, my mother was mad with me as I did not tell her before I bought it. (6) She also thought the phone was too expensive, and wanted me to return it to the shop. … “ The question is: what we want to extract from this review? The first thing that we may notice is that there are several opinions in this review. Sentences (2), (3) and (4) express three positive opinions, while sentences (5) and (6) express negative opinions. Then we also notice that the opinions all have some targets on which they are expressed. The opinion in sentence (2) is on iPhone as a whole, and the opinions in sentences (3) and (4) are on the “touch screen” and “voice quality” features of iPhone respectively. The opinion in sentence (6) is on the price of iPhone, but the opinion/emotion in sentence (5) is on “me”, not iPhone. This is an important point. In an application, the user may be interested in opinions on certain targets, but not on all (e.g., unlikely on “me”). Finally, we may also notice the sources or holders of opinions. The source or holder of the opinions in sentences (2), (3) and (4) is the author of the review (“I”), but in sentences (5) and (6) it is “my mother”. Reference [17] illustrates, with this example in mind, we can define sentiment analysis or opinion mining. It starts with the opinion target. Object and feature: In general, opinions can be expressed on any target entity, e.g., a product, a service, an individual, an organization, or an event. We use the term object to denote the target entity that has been commented on. An object can have a set of components (or parts) and a set of attributes (or properties), which we collectively call the features of the object [14]. A particular brand of cellular phone is an object. It has a set of components (e.g., battery and screen), and also a set of attributes (e.g., voice quality and size), which are all called features. Minqing Hu and Bing Liu [20] concluded that an opinion can be expressed on any feature of the object and also on the object itself. For example, in “I like iPhone. It has a great © 2013 ACEEE DOI: 03.LSCS.2013.2.79

touch screen”, the first sentence expresses a positive opinion on “iPhone” itself, and the second sentence expresses a positive opinion on its “touch screen” feature. Opinion holder: The holder of an opinion is the person or organization that expresses the opinion. In the case of product reviews and blogs, opinion holders are usually the authors of the posts. Opinion holders are more important in news articles because they often explicitly state the person or organization that holds a particular opinion. Opinion and orientation: An opinion on a feature f (or object o) is a positive or negative view or appraisal on f (or o) from an opinion holder. Positive and negative are called opinion orientations. In reference [1], it is discovered that with these concepts in mind, we can define a model of an object, a model of an opinionated text, and the mining objective, which are collectively called the feature-based sentiment analysis model. The general sentiment analysis model terminologies and their descriptions can be found in [2]. Generally, Opinion can be of the two types namely, direct and indirect opinion. Additionally, opinion can be of type explicit and implicit opinion. An explicit opinion on feature is an opinion explicitly expressed on f in a subjective sentence. An implicit opinion on feature is an opinion on feature implied in an objective sentence. The following sentence expresses an explicit positive opinion: “The voice quality of this phone is amazing.” The following sentence expresses an implicit negative opinion: “The earphone broke in two days.” Although this sentence states an objective fact, it implicitly indicates a negative opinion on the earphone. In general, objective sentences that imply positive or negative opinions often state the reasons for the opinions. In practice, not all five pieces of information in the quintuple described in [2] above need to be discovered for every application because some of them may be known or not needed. For example, in the context of online forums, the time when a post is submitted and the opinion holder are all known as the site typically displays such information. In the same sense, reference [4] focused on one type of opinion sources, customer reviews of products and proposed a novel visual analysis system to compare consumer opinions of multiple products. To support visual analysis, they designed a supervised pattern discovery method to automatically identify product features from Pros and Cons in reviews of format. A friendly interface is also provided to enable the analyst to interactively correct errors of the automatic system, if needed, which is much more efficient than manual tagging. The tasks of feature-level opinion mining usually include the extraction of product entities from product reviews, the identification of opinion words that are associated with the entities, and the determining of these opinions’ polarities (e.g., positive, negative, or neutral). In recent years, several approaches have been proposed such as rule-based and statistical methods on this subject, but few attentions have 49

Poster Paper Proc. of Int. Conf. on Advances in Information Technology and Mobile Communication 2013 opinions and there are even more expressions (possibly unlimited) that can convey these concepts. However, little in-depth study has been done on many of them. With the proliferation of social networking and ecommerce the information contained in the opinions/reviews expressed by the people has grown by leaps and bounds. Reference [18] presents an opinion search engine system that incorporates two novel opinion mining algorithms. The opinions are based on features and the orientation of these opinions is also largely based on the features rather than a product as a whole. People seem to like/dislike a specific product because of some feature associated with the product. The proposed framework not only classifies a review as positive or negative, but also extracts the most representative features of each reviewed item, and assigns opinion scores on them. Feature extraction and synonym grouping, they remain to be very challenging as studied by Bing Liu [3]. Object extraction is probably the easiest because many existing information extraction algorithms can be applied. Integration and matching of all 5 pieces of information required for sentiment analysis in the quintuple as given in [2] is still lacking, which is probably not surprising as the research community likes to focus on individual sub-problems. This leads us to the question of sentiment analysis accuracy, i.e., what is the accuracy of the current state-of-the-art algorithms? This question is not easy to answer because there are so many sub-problems. The proposed research work intends on real-life applications, to provide a completely automated solution. The key of the proposed research work is to fully understand the whole range of issues and pitfalls in sentiment analysis, cleverly manage them, and determine what portions can be done automatically and what portions need human assistance. Beyond what have been discussed so far, it is also important to deal with the issue of opinion spam (e.g., fake reviews). Opinion spam refers to writing fake or bogus reviews that try to mislead users or automated systems by giving untruthful positive and /or negative opinions in order to promote some target objects and /or to damage the reputations of some other objects [8]. Detecting such spam is needed because it can make sentiment analysis useless for decision making process. There is a real and huge need in the industry for such services to be implemented. This system aims to find what people like and dislike about a given product. Therefore how to find out the product features that people talk about is an important step[27]. However, due to the difficulty of natural language understanding, some types of sentences are hard to deal with as stated in [8, 25]. Let us see some easy and hard sentences from the reviews of a digital camera: “The pictures are very clear.”, “Overall a fantastic very compact camera.” In the first sentence, the user is satisfied with the picture quality of the camera, picture is the feature that the user talks about. Similarly, the second sentence shows that camera is the feature that the user expresses his/her opinion. While the features of these two sentences are explicitly

been paid to applying more discriminative learning models to achieve the goal. On the other hand, little research work has evaluated their algorithms’ performance for identifying intensifiers, entity phrases and infrequent entities [16]. This work particularly adopts the Conditional Random Fields (CRFs) model to perform the opinion mining tasks. Relative to related approaches, it has not only highlighted the algorithm’s ability in mining intensifiers, phrases and infrequent entities, but also integrated more elements in the model so as to optimize its training and decoding process. Hong Liu [10] proposed a model about internet public opinion hotspot detection and analysis, the main technique of categorization proposed was text categorization. According to the text properties of internet public opinion, introduced Vector Space Model to express text opinion. Text corpora are chosen from some new websites. It perform Kmeans clustering and Support Vector Machine (SVM) classifier on the documents, the experimental result shows that the efficiency and effectiveness of such method. Though, the use of Data mining techniques is introduced but refinement for each step of the approach proposed above is needed and further issues are not addressed; mainly, Dynamic monitoring technology is in demand which can monitor the web sites to detect change in time; Data cleaning is timeconsuming and labor-intensive; Web content analysis cannot stop at word frequency analysis because sometimes the result is poly-semantic. Moreover, it addresses a need for novel techniques that will summarize and analyze the relevant information in a principled and systematic way. Reference [19] anticipates the introduction of a collaborative framework that will further advance the state of the art and establish new targets for the next decade. Contradiction Analysis can possibly be the most demanding field for such a framework, as it utilizes most of the opinion mining methods, and at the same time defines its problems on data of various types, ranging from opposite sentiments to conflicting facts. This discussion gives us a good clue of the main tasks involved and technical challenges in sentiment analysis. The research community has studied individual sub problems of the sentiment analysis [6 and 7]. The most well studied sub problem is opinion orientation classification (i.e., at the document level, sentence level and feature level). Hui Wang, Jiansheng Chen et al. [11] presents a set of language patterns, which is composed of 22 rules, to extract two-noun phrases from customer reviews. Additionally, language rules can extract useful product features that human taggers fail to annotate as specified in [13]. The experimental results of [11] indicated that the accuracy of the classifiers benefits from the increasing of the text’s length and also varies. The existing reported solutions are still far from required. The main issue is that the current studies are still coarse. Not much has been done on finer details. For example, on opinion classification, there are many conceptual rules that govern © 2013 ACEEE DOI: 03.LSCS.2013.2.79

Poster Paper Proc. of Int. Conf. on Advances in Information Technology and Mobile Communication 2013 mentioned in the sentences, some features are implicit and hard to find. For example, “While light, it will not easily fit in pockets.” This customer is talking about the size of the camera, but the word “size” is not explicitly mentioned in the sentence. To find such implicit features, semantic understanding is needed, which requires more sophisticated techniques. However, implicit features occur much less frequent than explicit ones. Thus, mostly existing study on opinion analysis focus on finding features that appear explicitly as nouns or noun phrases in the reviews. To identify nouns/ noun phrases from the reviews uses the part-of-speech tagging. Opinion mining suffers from several different challenges, such as determining which segment of text is opinionated, identifying the opinion holder, determining the positive or negative strength of opinion [15]. Opinion mining is concerned with the human reviews, emotions and sentimental discussion. Everyone has their own perception and concern about a particular problem, issue, or topic. Opinionated text may be fake, irrelevant and or ambiguous information. Opinions are far harder than facts to describe. Opinion sources are typically informally written and highly diverse. Here, it gives clear indication about findings of opinion from the existing research work involved for opinion analysis. Their main contribution is to identify either opinion is positive, negative or neutral based on explicit opinion and its features identification only. A very little focus is given on implicit opinion and its implicit features too as described in above reviews of the digital camera. So the proposed research work will focus on identifying explicit as well as implicit opinions through their explicit and implicit features analysis.

opinion data is available in very large amount and from this huge data, extracting useful and effective information is a challenging task [23, 24]. As Data mining (DM) techniques are useful to do such kind of tasks effectively. So in the proposed research work, it intends to make use of DM techniques for the opinion analysis. Even various natural language processing techniques are efficient for fast document & its content processing. Thus, to make the proposed research model more accurate, efficient, these techniques will be suitable. It concludes the objective of the proposed research work is: “To build an automated opinion discovery system to enhance performance of sentiment or opinion analysis based on feature extraction sentiment analysis sub technique, natural language processing and data mining techniques in an integrated way.” IV. SYSTEM MODEL The motivation in the research work is to develop an automated opinion or sentiment analysis system that will not only mine the opinions but will also extract useful information related to the item’s features and use it to rate them as positive, neutral, or negative [9]. This feature based opinion mining will help the user to focus on the features of the opinion/ product he/she is interested in. Fig.1 gives an architectural design for automated sentiment or opinion analysis system The system performs the opinion analysis in three main steps: Data collection and its preprocessing, opinion mining through opinion mining engine and generation of opinion status. The inputs to the system are in the form of datasets where dataset will include product name and an entry page for all the reviews of the products. The output is the opinion status of the reviews. i.e. positive, negative or neutral opinion about the product. Given the inputs, the system first downloads all the reviews, and puts them in the dataset for further processing. Following section briefly outline the main components of the proposed system as shown in Fig.1 Data preprocessing The data from the dataset is preprocessed so as to set the data in the format which is acceptable to the data processing techniques .The outcome of this step will give us formatted dataset which will be the input for the opinion miming engine. As studied in literature review, most of the existing dataset or database files available for opinion mining are in the form of web pages formats [8], either in HTML or XML tags. So before opinion mining, it is necessary to filter out such tags to get opinion dataset only. This step is also aimed towards such data preprocessing before to set the data in the format which is acceptable to the further data processing techniques Opinion Mining Engine This is the main component of the proposed system model. It further consists of two major steps of its computation namely, Feature Extraction and Opinion Direction Identification. The illustration of these steps is

III. OBJECTIVES As stated earlier Sentiment analysis is multi-faceted problem thus, there is need to build integrated system that tries to deal with all multi-faceted problem altogether. To address this, the main objectives of this proposed research work are enlisted below: The first aim is to find the key features i.e. explicit and implicit features about an object that are talked about in multiple reviews and their analysis. So it means that the proposed opinion analysis system will concentrate on the feature extraction to achieve effective and useful opinion or sentiment analysis. It leads to the requirement of integrated system that will deal with these multi-faceted issues such as object identification, feature extraction and synonym grouping, opinion orientation, classification and integration in the integrated fashion so that it will do sentiment or opinion analysis effectively using explicit as well as implicit features of opinion. At the same time, the most of existing opinion discovery systems are developed using Artificial Neural Network, SVM, Soft-Constraints and Entropy Model, etc. techniques [21, 22]. Today, if one wants to do effective opinion analysis, the © 2013 ACEEE DOI: 03.LSCS.2013.2.79

Poster Paper Proc. of Int. Conf. on Advances in Information Technology and Mobile Communication 2013 sentence. The output of this step is status of the reviews. i.e. positive, negative or neutral opinion about the product. V. USEFULNESS There is a real and huge need in the industries for accurate reviews for their products to improve customer satisfaction. In IT industries, the proposed system will be useful to reduce the issue of opinion spam. The practical need and the technical challenges will keep the field vibrant and lively. Government intelligence is another application that has been considered. For example, it has been suggested that one could monitor sources for increases in hostile or negative communications. Sentiment-analysis technologies for extracting opinions from unstructured human-authored documents would be excellent tools. Besides reputation management and public relations, one might perhaps hope that by tracking public viewpoints, one could perform trend prediction in sales or other relevant data. Sentiment or opinion analysis would be the basis for the creation of automated websites. VI. IMPLEMENTATION DETAILS Figure.1 Architectural Design for Automated Sentiment or Opinion Analysis System

given in next session: The feature extraction, which is the first and foremost task of the proposed work, it extracts “frequent” features that a lot of people have expressed their opinions on in their reviews, and then finds those infrequent ones. The opinion direction identification takes the generated features and states the opinions about the feature through the opinion status component of the system. In this step, to perform part of speech tagging, use of the NLProcessor like linguistic parser is proposed, which parses each sentence and yields the part-of-speech tag of each word (whether the word is a noun, verb, adjective, etc) and identifies simple noun and verb groups (syntactic chunking). It will help to identify the expressed opinion is implicit or explicit type. The next step is to find features that people are most interested in. In order to do this, here proposed system will use association rule mining to find all frequent itemsets. Not all frequent features generated by association mining are useful or are genuine features. There are also some uninteresting and redundant ones. Feature pruning aims to remove these incorrect features. Opinion words are words that people use to express a positive or negative opinion. Observing that people often express their opinions of a product feature using opinion words that are located around the feature in the sentence, we can extract opinion words from the review dataset using all the remaining frequent features (after pruning). After opinion features have been identified, one can determine the semantic orientation (i.e., positive or negative) of each opinion sentence. This consists of two steps: (1) for each opinion word in the opinion word list, there is need to identify its semantic orientation and (2) then decide the opinion orientation of each sentence based on the dominant orientation of the opinion words in the 52 © 2013 ACEEE DOI: 03.LSCS.2013.2.79

The proposed design architecture simulation work is already started using Python and it is in its initial stage. Even visualization of same collected dataset is done through WEKA data mining tool. The data collection, its preprocessing details are given below. Review Data Collection The required dataset is collected from the internet which is freely available for the research study. This dataset is a subset of the opinion mining datasets released by Dr. Bing Liu’s group from University of Illinois at Chicago. Their dataset is available from http://www.cs.uic.edu/~liub/FBS/ sentiment-analysis.html Collected Dataset Format Details This dataset (HL-11prods-2200comments.xml) has classification labels (from the manual annotation process done by Dr. Bing Liu’s group) for the “opinion” class - which marks whether or not a review comment consists of any subjective evaluation of one or more features of the product or the product itself [26]. The format of the file is pseudoXML. Each review comment is represented by an <instance>...</instance> tag in the file. The complete set of 2,200 instances is enclosed in an outermost level <instances>...</instances> tag. The <instance> tag has two attributes - “id” and “subpop.” “id” is a unique identifier given to each instance. “subpop” is a string that identifies the product name for which the review comment was written. Within each <instance> tag, the “cname” attribute in the <class> tag contains the classification label - POS stands for the opinion class, and NEG for the non-opinion class. The <text>...</text> tag contains the actual text of the review comment. Data preprocessing From this dataset format, we have been extracted data from XML file and made it suitable for proposed design

Poster Paper Proc. of Int. Conf. on Advances in Information Technology and Mobile Communication 2013 architecture in its data preprocessing step and applied feature extraction module on it. Feature Extraction In this step, the use of text processing toolkit NLTK is proposed along with Python to do natural language processing for sentiment analysis. The results are in primary stage.

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CONCLUSION Finally, despite of the challenges, the opinion mining or sentiment analysis has made significant progress over the past few years. This is evident from the large number of startup companies that provide sentiment analysis or opinion mining services. The opinions can be taken from all possible means through media of newspaper, focus groups, blogs, sms services and web sites also. Especially, it can be taken in different languages other than English for the region wise or country wise customer opinion reviews. So to enhance the performance of sentiment or opinion analysis system, the proposed design architecture of sentiment analysis will integrate the multiple techniques of opinion analysis altogether and will enrich the trust of people on such technology. REFERENCES [1] Arjun Mukherjee, Bing Liu, “Aspect extraction through SemiSupervised modeling,” In support National Science Foundation under grant no. IIS-1111092, pp.1 - 10, 2012. [2] B. Liu , Sentiment Analysis and Subjectivity Handbook of Natural Language Processing, Second Edition, 2010. [3] Bing Liu, “Sentiment analysis: a multi-faceted problem,” IEEE Intelligent Systems, pp.1-5, 2010. [4] Bing Liu, Minqing Hu, Junsheng Cheng, “Opinion observer: analyzing and comparing opinions on the web,” International World Wide Web Conference Committee (IW3C2), ACM, pp.1-10 , 2005. [5] B. Pang and L. Lee, “Opinion mining and sentiment analysis,” Foundations and Trends in Information Retrieval 2 (1-2), pp. 1–135, 2008. [6] Chee Kian Leong, Yew Haur Lee, Wai Keong Mak, “Mining sentiments in SMS texts for teaching evaluation,” Expert Systems with Applications, pp. 2584–2589, 2012. [7] Chunxia Yin, Qinke Peng, “Sentiment Analysis for Product Features in Chinese Reviews Based on Semantic Association,” International Conference on Artificial Intelligence and Computational Intelligence, pp.82-85, 2009. [8] Fazel Keshtkar, Diana Inkpen, “Using sentiment orientation features for mood classification in blogs,” IEEE 978-1-42444538-7, pp. 1-6, 2009. [9] Hanxiao Shi, Guodong Zhou, Peide Qian , “An attribute based sentiment analysis system,” Information Technology Journal ISSN 1812-5638, pp.1607-1614, 2010.