Abstract — At present lots and lots of people share their thoughts in socialmedia and websites, making it a valuable platform for analyzing the peoplessentiment of an object, such analysis may provide a key information for thegrowing industries on various specific domains. Currently the use ofsentimental analysis has spread to most of the services and making developmentof technological industries into existence which helps them with finding theirtarget customers and stripping down their reviews to more helpful methods. Thispaper aims to create a specific software which is used to inject any kind of user’sinformation or customers reviews or even the day to day news updates andfinally gives us a statically analyzed data which helps the end user todetermine their needs.Keywords—Sentimental analysis; SoftwareInjection; Statically analyzed data.                                                                                                                                                I.      Introduction Today the use of internet is growing in an enormous way,makes every individual easier and comfortable to use the internet and get therelevant data as required. Most companies and product manufactures useSentimental Analysis in order obtain the product reviews from customers andhelps to improve the quality of their products and opinion method using thesetwo the product manufactures gets their advancements and beneficent gains.

Presently the sentimental analysis is done where the customers and the usersgives their views and reviews through social media and social networking sites andnow its main target is to make computer able to identify and create emotionslike human being. This paper aims to create a software specifically forSentimental analysis with any kind of data given by the user not only theSocial Media sites, this paper presents the literature review and theimplementation of Sentimental Analysis with a software or an web application ,thereview is based on the data collected from various research papers, tools andweb sources that will strongly assist in easy referencing                                                                                                                              II.    LITERATURE SURVEY Sentimentanalysis has been handled as a Natural Language Processing task at many levelsof granularity. And its theprocess of computationally identifying and categorizing opinions expressed in apiece of text, especially in order to determine whether the writer’s attitudetowards a particular topic, product, etc. is positive, negative, or neutral.Now it has been widely used area over years and still there’s a lot to be researchedwith it.

In an investigation they collected a millions of data abouttechnological posts from Twitter and demonstrate that many latent variationcharacteristics of the technological currently changed over time from the data.Logunov and Pancheko generated Twitter sentiment indices by analyzing astream of Twitter messages and categorizing messages in terms of emotions,pictorial rep of facial expressions in messages. Based on emotions theygenerated daily indices. Then they explored time-series properties of theseindices by focusing on seasonal patterns, persistence and conditional forms.Zhang, Parikh chosen a global ecommerce platform (eBay) and a global socialmedia platform (Twitter).

They quantified the characteristics of the twoindividual trends as well as the correlations between it. They providedevidences that about 5% of general eBay query streams show strong positivecorrelations with the corresponding Twitter mention streams, while thepercentage jumps to around 25% for trending eBay query streams. Another interestingthing is that Gomide analyzed how Dengue epidemic is reflected on Twitter andto what extent that information can be used for the sake of surveillance.Dengue is a mosquito-borne infectious disease that is a leading cause ofillness and death in tropical and subtropical regions, including Brazil.

Theyproposed an active surveillance methodology that is based on four dimensions:volume, location, time and public perception. First they explored the publicperception dimension by performing sentiment analysis. This analysis enables theirto filter out content that is not relevant for the sake of Dengue surveillance.Kooti and Mason described the process in detail, highlighting the factorsthat come into play in deciding which variation individuals will adopt. Theirclassification analysis demonstrates that the date of adoption and the numberof exposures are particularly important in the adoption process, while personalfeatures (such as the number of followers and join date) and the number ofadopter friends have less discriminative power in predicting adoptions. Theydiscussed implications of these findings in the design of future Webapplications and services. Lavanya and Varthini discussed that in order toget useful data it becomes necessary to apply NLP techniques which make it easyfor the people to make decisions at the time of buying products or contractingservices. All the users are not concerned with all features of a product.

Hencethis research proposed a feature based sentiment classification method thathelps a user to make decisions easily based on their features of interest.                                                                                      III.   Data Description of Injecting application Thedata for this software is collected from the user and then uploaded to the serversthen our Sentimental Analysis algorithm goes on with the data collected andworks on it and gives the result according the user specified instructions andvalues provided if any, the application lets the user to upload his data andthe data will be secured, for instance Twitter is a social networking andmicroblogging service that allows users to post real time messages, calledtweets. Tweets are short messages, restricted to 140 characters in length. Dueto the nature of this microblogging service (quick and short messages), peopleuse acronyms, make spelling mistakes, use emoticons and other characters thatexpress special meanings.

Following is a brief terminology associated withtweets. Emoticons: These are facial expressions pictorially represented usingpunctuation and letters; they express the user’s mood. Target: Users of Twitteruse the “@” symbol to refer to other users on the microblog. Referring to otherusers in this manner automatically alerts them. Hashtags: Users usually usehashtags to mark topics.

This is primarily done to increase the visibility oftheir tweets.Herethey acquire 11,875 manually annotated Twitter data (tweets) from a commercialsource. They have made part of their data publicly available. For informationon how to obtain the data, see Acknowledgments section at the end of the paper.They collected the data by archiving the real-time stream.

No language,location or any other kind of restriction was made during the streaming process.In fact, their collection consists of tweets in foreign languages. They useGoogle translate to convert it into English before the annotation process.

Eachtweet is labeled by a human annotator as positive, negative, neutral or junk.The “junk” label means that the tweet cannot be understood by a humanannotator. A manual analysis of a random sample of tweets labeled as “junk”suggested that many of these tweets were those that were not translated wellusing Google translate. We eliminate the tweets with junk label forexperiments. This leaves us with an unbalanced sample of 8,753 tweets. We usestratified sampling to get a balanced data-set of 5127 tweets (1709 tweets eachfrom classes positive, negative and neutral).                                                                                                                  IV.

   Resources and PreprocessingHere we are going to introduce many resources forpre-processing of the data to be processed.We are going to prepare a dictionarybased on the pre-processing data which are already available in the internetfor instance Wikipedia. For example, a data of some category is labeled aspositive whereas other specific category is labeled as negative. We assign eachdata label from the following set of labels: Extremely-positive,Extremely-negative, Positive, Negative, and Neutral. We compile an acronymdictionary from an online resource The dictionary has translations for thousandsof acronyms. For example, lol is translated to laughing out loud. Wepre-process all the data sent by user as follows: a) replace all the userdefined data with a their sentiment polarity by looking up the data dictionary,b) replace all URLs with a tag ||U||, c) replace all negations (e.

g. not, no,never, n’t, cannot) by tag “NOT”, and e) replace a sequence of repeatedcharacters by three characters, for example, convert coooooooool to coool.And alot more of specific functions based on the data provided We do not replace thesequence by only two characters since we want to differentiate between theregular usage and emphasized usage of the word.  Words/Data Polarity Wow,Good Positive Great,Excellent Extremely-Positive Not so good Negative Very Bad Extremely-Negative Ok Neutral  Table 1: Part of the dictionary of POSdata Such data’s are collected and a standard tag set isdefined  by the Penn Treebank foridentifying punctuation. We record the occurrence of three standard twittertags: emoticons, URLs and targets. The remaining tokens are either non Englishwords (like coool, zzz etc.

) or other symbols.     We alsosegregated the data according to the percentage of tokens, Stop words, Englishwords, Punctuation marks ,Capitalized words, tags on data, percentages, exclamationmarks ,negations etc..                                                                                                                                V.    Design of Application     We propose anapplication which does all the features to be done to give the user hisrequired results and the algorithms are worked on the backend of the programand at the backend of the application a kernel of tree is included with thepython algorithm which makes easier for the user to get the results. n.

Forcalculating the similarity between two trees we use a Partial Tree (PT) kernelfirst proposed by Moschitti (2006). A PT kernel calculates the similaritybetween two trees by comparing all possible sub-trees. This tree kernel is aninstance of a general class of convolution kernels. Convolution Kernels, firstintroduced by Haussler (1999), can be used to compare abstract objects, likestrings, instead of feature vectors. This is because these kernels involve arecursive calculation over the “parts” of abstract object.

This calculation ismade computationally efficient by using Dynamic Programming techniques. Byconsidering all possible combinations of fragments, tree kernels capture anypossible correlation between features and categories of features. Thus thesoftware/application contains the investigated two kinds of models: tree kerneland feature based models and demonstrate that both these models outperform theunigram baseline. We tentatively conclude that sentiment analysis for usersdata is not that different from sentiment analysis for other genres.     Theapplication will be very user friendly such that the user just has to uploadthe data and wait for few minutes which is according to the data given and cansee the end results in a graphical representation like a graph which stateswith the information clearly as of users needs and the information is resultedby using the users data with the backend algorithm and getting the results in thefrontend for the users feed.                                                                                                           VI.   Existing methods and featuresExisting approaches tosentiment analysis can be grouped into three main categories: knowledge-basedtechniques,statistical methods, and hybrid approaches.

 Knowledge-basedtechniques classify text by affect categories based on the presence ofunambiguous affect words such as happy, sad, afraid, and bored. Someknowledge bases not only list obvious affect words, but also assign arbitrarywords a probable “affinity” to particular emotions. Statisticalmethods leverage on elements from machine learning such as latentsemantic analysis, support vector machines, “bag of words” and Semantic Orientation — Pointwise Mutual Information (See PeterTurney’s work in this area). More sophisticated methods try todetect the holder of a sentiment (i.e., the person who maintains that affectivestate) and the target (i.e.

, the entity about which the affect is felt). Tomine the opinion in context and get the feature about which the speaker hasopined, the grammatical relationships of words are used. Grammatical dependencyrelations are obtained by deep parsing of the text. Hybridapproaches leverage on both machine learning and elements from knowledge representation such as ontologies and semanticnetworks in order to detect semantics that are expressed in asubtle manner, e.g., through the analysis of concepts that do not explicitlyconvey relevant information, but which are implicitly linked to other conceptsthat do so.                                                                                                                                                      VII.  FeaturesOur features can be divided into three broadcategories: ones that are primarily counts of various features and thereforethe value of the feature is a natural number ? N.

Second,features whose value is a real number ? R. These are primarily features thatcapture the score retrieved from DAL. Thirdly, features whose values areboolean ? B. These are bag of words, presence of exclamation marks andcapitalized text. Each of these broad categories is divided into twosubcategories: Polar features and Non-polar features.

We refer to a feature aspolar if we calculate its prior polarity either by looking it up in DAL(extended through WordNet) or in the emoticon dictionary. All other featureswhich are not associated with any prior polarity fall in the Nonpolar category.Each of Polar and Non-polar features is further subdivided into two categories:POS and Other.

POS refers to features that capture statistics aboutparts-of-speech of words and Other refers to all other types of features.            Notonly the programmers usual people can also be able to use our application tofind various sentimental in their workways and according to their data used.            Forinstance: Determining the weather and climatic pattern news of the whole yearand comparing with the previous year used in metrological purposes and environmentalists.            Findingthe hot topic in a news data currently and segregating whether the news/currentstream is positive or negative.                                                                                                                                                VIII. ConclusionToday social networking on Internet have become anessential part for everyone. By making Internet useful with the help ofapplying opinion or sentiment mining techniques, a customers and companymanufacturers can get reviews of the product and get feedback from thecustomers so that the company can improve its product to satisfy his customersapproximately 100% respectively.

Even though a lot of work have been done inthis area but still it acts as a fertile area for new researchers. Thusthrough our Application which is injected with sentimental analysis algorithmsevery individual can be able to find their required positives and negatives orneutral with their data, this application helps to get a clear idea and gives acritical information required for the user. As well as, product manufacturers can obtain productreviews from customers to improve the quality of their products on timelyfashion and opinion method bringing together these two so that both of them cando for their advancement and beneficent. IX.

REFERENCESA.    Logunov, V. Panchenko, “Characteristics andPredictability of Twitter Sentiment Series”, 19th International Congress onModelling and Simulation, 2011, pp. 1617-1623B.    H. Zhang, N.

Parikh2, G. Singh, N.Sundaresan, “Chelsea Won, and You Bought a T-shirt: Characterizing theInterplay Between Twitter and ECommerce”, Proceedings of the IEEE/ACMInternational Conference on Advances in Social Networks Analysis and Mining,2013, pp. 829- 836C.    J. Gomide, A.

Veloso, W. Meira Jr, “Denguesurveillance based on a computational model of spatio-temporal locality ofTwitter”, Proceedings of the ACM WebSci’11, 2011, pp. 1-8D.   . F.

Kooti, W. A. Mason, “PredictingEmerging Social Conventions in Online Social Networks”, Proceeding of the 21stACM international conference on Information and knowledge management, 2012, pp.

445- 454 E.     S.K.Lavanya and B.P.Varthini, “SentimentClassification Of Web Opinion Documents”, IEEE International Conference onElectronics and Communication Systems (ICECS), 2014, pp.1 – 5.


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