Food & Beverages

Sentiment Classification by Sentence Level Semantic Orientation using SentiWordNet from Online Reviews and Blogs

Sentiment Classification by Sentence Level Semantic Orientation using SentiWordNet from Online Reviews and Blogs Aurangzeb khan, Baharum Baharudin Department of Computer and Information Sciences, Universiti
of 9
All materials on our website are shared by users. If you have any questions about copyright issues, please report us to resolve them. We are always happy to assist you.
Sentiment Classification by Sentence Level Semantic Orientation using SentiWordNet from Online Reviews and Blogs Aurangzeb khan, Baharum Baharudin Department of Computer and Information Sciences, Universiti Teknologi PETRONAS Perak, Malaysia. 539 Abstract: Sentiment analysis is the procedure by which information is extracted from the opinions, appraisals and emotions of people in regards to entities, events and their attributes. In decision making, the opinions of others have a significant effect on customers ease in making choices regards to online shopping, choosing events, products, entities. In this paper, the rule based domain independent sentiment analysis method is proposed. The proposed method classifies subjective and objective sentences from reviews and blog comments. The semantic score of subjective sentences is extracted from SentiWordNet to calculate their polarity as positive, negative or neutral based on the contextual sentence structure. The results show the effectiveness of the proposed method and it outperforms the machine learning methods. The proposed method achieves an accuracy of 87% at the feedback level and 83% at the sentence level for comments and 97% at feedback and 86 % at sentences for customer reviews. Keywords: Sentiment Analysis, Opinion Mining, Classification. Blog Mining. 1 Introduction Essential for online services, that exist today, is the abundance of applications for sentiment analysis. Mining customer reviews or gathering feedback from opinions about a given product (e.g., digital camera, car, mobile phone, etc.) can give companies valuable information as to the satisfaction or dissatisfaction of their customers. This information is also immensely valuable for customers in their decisions to purchase a particular product. Furthermore, sentiment analysis enables trend watchers, marketing research teams and recommendation systems to track emotions or opinions over time; tracking of online trends (mood) provides interesting as well as valuable data for these groups. In the case of moderating, opinion mining has also proven to be very useful whereby the ability to react quickly and efficiently to messages which have been posted on forums or discussion boards wherein dissatisfied consumers discuss product deficiencies. Moreover, being able to respond appropriately to any heated debates or flame wars that may be going on is also a benefit. This study focuses on sentiment analysis at the sentence level using lexical rule based method for different type data like (blogs, movie reviews, social communication networks e.g. twitter, and product reviews). KDT (knowledge discovery in texts) or text data mining or text mining are terms used for the mining of unstructured or semi-structured data. It is a slightly new sub-discipline of data mining that considers textual data. The fact is that, text data mining is an intermediate evolutionary lexical form, [1].The Majority of the online information about data mining is misleading. Such ambiguous/misleading information implies to the mining metaphor that it is like extracting precious nuggets of ore from otherwise worthless rock as found in or finding gold hidden in mountains of textual data in (D orre et al. 1999).A more narrow definition of what text mining does is needed to distinguish it from the traditional IR, which is basically information access as argued by Hearst [1]. Retrieval of documents relevant to the information needs of a user, is the primary concern of the traditional IR (perhaps a more appropriate name would be data retrieval); however, the user is left on his/her own to find the desired information in the documents. In Hearst s opinion, data mining has not only directed dealings with the information, but it also attempts to uncover or glean previously unknown, information from the data (text). different linguistic levels (words, sentences and documents)highlighting the key differences between supervised machine learning methods, that rely on annotated corpora or corpusbased, and unsupervised/lexicon-based methods in sentiment classification. Three main steps are always involved in the process of text mining and sentiment classification; they are (a) acquiring texts which are relevant to the area of concern usually called IR; (b) presenting contents collected from these texts in a format that can be processed, such as statistical modelling, natural language processing, etc.; and (c) actually using the information in the presented format,[3] [4] communities [5] [6]. This is true for the management of news groups, maintenance of web directories and even s. A good tutorial on hypertext mining can be found in [7]. Mining from services is also a growing area in the field of information extraction. This is greatly in part due to the staggering number of online services like Usenet, digital libraries, news groups, customer comments and reviews and mailing lists which are popping up all over the Web. It should be noted that, a very thin line separates Web content mining from IR; this is because there is a deep association of WCM with intelligent IR Mining. On the other hand, it is claimed by some that IR on the Web is a part of WCM. Two strategies are used for WCM; one is the direct mining of document content as in Web page content mining and the other is improving on the search for content using tools like search engines as in search result mining. This makes the job of the search engine closely associated with WCM [8]. In this work, we proposed a domain independent rule based method for semantically classifying sentiment from online customer reviews and comments. The method is effective as it takes reviews, checks individual sentences and decides its semantic orientation considering the sentence structure and contextual dependency of each word. The rest of the paper is organized as follows: Section-2 presents the related research of the proposed work. Section-3 describes the proposed method with all steps. Section-4 highlights the results and finally Section-5 concludes the proposed method. 2 Background and Related Work The early work of sentiment analysis began with subjectivity detection, dating back to the late 1990 s. Later, it shifted its focus towards the interpretation of metaphors, point of views, narrations, affects, evidentiality in text and other related areas. Shown below is the literature describing the early works of subjectivity and detection of affects in the text. With the increase in internet usage, the Web became a source of importance as text repositories. Consequently, a switch was slowly made away from the use of subjectivity analysis and towards the use of sentiment analysis of the Web content. Sentiment analysis has now become the dominant approach used for extracting sentiment and appraisals from online sources. Separating non-opinionated, neutral and objective sentences and texts from subjective sentences carrying heavy sentiments is a very difficult job; however, it has been explored earnestly in a closely related yet separate field, (J. M. Wiebe, 1994). It concentrates on making a distinction between 'subjective' and 'objective' words and texts; on one hand, the subjective ones give evaluations and opinions and on the other, the objective ones are used to present information which is factual (J. Wiebe, Wilson, R. Bruce, Bell, & Martin, 2004) (J. Wiebe & Riloff, 2005). This is different than sentiment analysis in regards to the set of categories into which language units are classified by each of these two analyses. Subjectivity analysis focuses on dividing language units into two categories: objective and subjective, whereas sentiment analysis attempts to divide the language units into three categories; negative, positive and neutral. The area of concentration in some of the early works was with subjectivity detection only (J. M. Wiebe, 2000). With the passage of time and a need for better understanding and extraction, momentum slowly increased towards sentiment classification and semantic orientation. Like other developing fields of research today, sentiment analysis terminology is yet to be matured; moreover, just attempting to define a sentiment can be difficult to accomplish [13]. The words sentiment [14][15], polarity [16] [12] [17], opinion [18], [19], 540 semantic orientation [12] [20], attitude [21] and valence [22] are used to represent similar if not the same ideas. These words are, more often than not, used either to make reference to various aspects of one particular phenomenon, an example being [23] [15] where sentiment is defined as an affective part of opinion, or simply used as synonyms for each other without any true definition of their own. Furthermore, some of these words can be confusing because of their multiple meanings already in linguistic tradition (e.g. polarity, valence) and therefore are confusing. For our present work, the focus is on capturing expressed sentiment in a text as negative, positive or neutral; therefore, we will refer this domain of research as sentiment analysis. Our preference of using the term sentiment analysis is due to the fact that: (1) the possibility of confusing this work with research in other areas is not likely because the term is not associated with any other research tradition, (2) the kind of data which is extracted from the texts is accurately reflected (unlike in the case of opinions which could also possess a topical component), and (3) it is parsimonious and precise [24] [25]. Recently, there has been a change of attitude in the field of sentiment analysis whereby the concentration is now on classification, which has added a third category known as neutrals [26], [15]. Therefore, it is no longer focused on the binary classification of only positive/negative [20]. Through empirical observations, there came a realization that it is much easier to separate positive elements from negative ones than it is to differentiate positives or negatives from neutrals. Majority of disagreements amongst human annotators as well as the errors resulting from utilizing automatic systems are associated with attempting to separate neutral words, sentences or texts from those that are either negative or positive [15]. Moreover, a problem arises from the meaning attributed to the term neutrals. This is because lack of opinion [27] as well as a sentiment that lies between positive and negative [13] are both meanings of Neutrality used in related literature. The latter definition is favoured by sentiment analysis while the field of subjectivity analysis mostly uses the former interpretation. However, it is the latter meaning of the word that will be utilized in this dissertation [24] [25]. A rating inference as a metric-labelling problem was developed by [28]. They achieved this by first applying two n-array classifiers, which included onevs.-all (OVA) SVM and SVM regression, in order to classify reviews in regards to multi-point rating scales. After applying the classifiers, a metriclabelling algorithm was utilized so that the results of the n-array classifiers were completely changed in order to guarantee that like items receive like labels. A similarity function was determined from this. It is true that a typically used similarity function in topic classification is the overlapping of terms; however, when attempting to identify reviews having like ratings, it is not particularly effective [28]. The Positive Sentence Percentage (PSP) similarity function was subsequently introduced which calculates the number of sentences which are considered positive divided by the number of sentences in a review that are considered to be subjective. Results of experiments generally have shown an improvement in n-ary classifier performance when making use of metric-labelling with PSP. Pang and Lee s work was later augmented by [29] where they used transductive semi-supervised learning in their study. It was shown that classification accuracy could be improved upon with the help of reviews without user-specified ratings, in other words, unlabelled reviews [24]. A kernel regression algorithm which was introduced by [30] 2007, made use of order preferences of unlabelled data and was successfully applied to sentiment classification. The order preference of a pair of unlabeled data, x i and x j, indicates that x is preferred to x to some degree, even i j though the exact preferences for x and x are i j unknown. For example, in the framework of sentiment analysis, when presented with two reviews of unknown rating values, it is quite possible to determine which review is more positive. They executed their algorithm with the rating inference problem. As a result, it was evidenced that by utilizing order preferences the performance of rating inference was much better than standard regression [24] [25]. Corpus-based machine learning method or methods based on compilations are able to compile lists of negative and positive words with a high accuracy of up to 95%. However, in order to reach their full potential, most of these approaches need immense annotated training datasets. Corpus-based methods can overcome some of these limitations by utilizing dictionary-based approaches since these approaches depend on existing lexicographical resources (such as WordNet) to provide semantic data in regards to individual senses and words [31] [24] [25]. [32] suggested that when analysing sentiment, semantical similarity does not necessarily imply sentimental similarity. This suggestion was made on the basis of statistical observations from a compilation of movie reviews. Subsequently, a method for determining the semantic orientation of an opinion is proposed on the basis of relative frequency. An estimation of the opinion strength of a word and the semantic orientation in regards to a sentiment class and its relative frequency of appearance in that class is carried out using this method. For example, if the word best appeared 8 times in Positive reviews and 2 times in Negative reviews, its strength with respect to Positive semantic orientation is then 8/(8+2) = 8 [24]. Introduction of new features, that are conceptually related to keyphrase-frequency were done in [33]. On the basis of the candidate phrases in the input document, these new features can be generated, by issuing queries to a Web search engine. An improvement in keyphrase extraction has been 541 experienced with these new although they are neither domain specific nor training-intensive. The feature values are calculated from the number of hits for the queries (the number of matching Web pages). A large collection of unlabeled data, approximately 350 million Web pages without manually assigned key phrases, has been mined for lexical knowledge to derive these new features. Simple methods for combining individual sentiments [34] and supervised [35] statistical techniques were proposed which can measure sentiment on the phrase or sentence level using opinion oriented words. Another method, proposed by [36], makes use of both lexical and syntactic features for sentiment analysis and is a machine learning approach. This method, however, missed pertinent contextual information which indicates that the individual sentence itself is vital when extracting semantic orientation. An alternative method was suggested by [37] for utilizing WordNet s synonymy relations for tagging words with Osgood's three semantic dimensions. The shortest path joining a particular word to the words good and bad was calculated through WordNet relations in order to assign values of positive or negative to the word. Dictionary-based methods for sentiment classification at the word-level have no need of large corpora, or search engines having special functionalities. Rather, they depend on readily available lexical resources existing today such as WordNet. They are able to compile comprehensive, accurate and domain-independent word lists containing their sentiment and subjectivity annotated senses. Such lists provide a vital resource for sentence or text sentiment classification and because of early compilation they are able to increase efficiency of sentiment classification at text and sentence level. In contrast to the other works this work presents sentence level lexical/dictionary knowledgebase method to tackle the domain adaptability problem for different types data [31] [6]. Dictionary-based techniques make use of the data found in references and lexicographical resources, such as WordNet and the thesaurus which can be used for assigning sentiments to a large number of words. Majority of these methods utilize various relationships between words (synonymy, antonymy, hyponymy /hyperonymy) in order to find the seed words and other entries as described earlier. The data existing in dictionary definitions is made use in word-level sentiment orientation in some of the recent methods. For semantic orientation lexical based semantic terms are extracted using dictionaries like SentiWordNet, ConceptNet etc. for the sentence level classification. According to [13]. The first try at employing WordNet relations in word sentiment annotation was made by [15][18]. They made the suggestion about an extension to lists of manually tagged positive and negative words by adding to the list the synonyms for those words. They began with just 54 verbs and 34 adjectives. The method was applied in two occurrences and acquired 6079 verbs and adjectives. Then, on the basis of the strength of sentiment polarity which had been assigned to each word, the words which had been acquired were ranked. This strength-of-sentiment score or rank for each word was calculated by maximizing the probability of the category of the word s sentiment in regards to its synonyms [24] [25]. Semantic characteristics, like word sentiment, of each word are greatly acknowledged as good indicators of semantic characteristics of a phrase or a text that contains them, e.g. in (B. Baharudin, 2010) [20]. A sentence or text level sentiment annotation system uses words as indicators (features) of sentiment and therefore, requires the creation of words lists annotated with sentiment markers. The research on word-level sentiment annotation has produced a number of such lists of words that were manually or automatically tagged as sentiment or classified as related to sentiment. [39] [19] suggested a method that would use different information occurring at the same time in order to acquire words related to opinion (e.g., disapproval, accuse, commitment, belief) from texts as a way to carry out analysis of subjectivity at the word level. Two different techniques were used. The log-likelihood ratio is computed with the first technique; using data obtained by calculating how often words obtained from one sentence occur with seed words taken from [40]. Relative frequencies of words found in documents, either subjective or objective, are computed by using the second technique. When NLP and statistical techniques are utilized, much importance is given to sentiment analysis at the word or feature level because it is an analysis of the text with the most detail. The semantic orientation of a phrase or an opinion word is determined by the techniques proposed by [18] and [41]. Several researchers used a preset seed word to enable extraction of opinion-oriented words and features [42] [43] and form a list used for semantic orientation, extraction and classification of opinion. Determining the polarity and subjectivity of a text is not the only aim of sentiment analysis. On the contrary, what the author of the text specifically likes or dislikes regarding an object is also of importance [44]. Our main focus here is to discuss sentence and document level sentiment analysis. Sentence level analysis decides what the primary or comprehensive semantic orientation of a sentence is while the primary or comprehensive semantic orientation of
Similar documents
View more...
Related Search
We Need Your Support
Thank you for visiting our website and your interest in our free products and services. We are nonprofit website to share and download documents. To the running of this website, we need your help to support us.

Thanks to everyone for your continued support.

No, Thanks

We need your sign to support Project to invent "SMART AND CONTROLLABLE REFLECTIVE BALLOONS" to cover the Sun and Save Our Earth.

More details...

Sign Now!

We are very appreciated for your Prompt Action!