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(Review Article) Emotion Mining from Text in Social Media U. R. Rathor1, H. D. Chande2 1,2 Department of Computer Engineering, HJD Institute of Technical Education Research Gajod, At.

Kera, Kutch, Gujarat, INDIA Abstract Nowadays use of individuals are so much engaged in online activities most of their online time are Social Networking interaction. They also share their thoughts, emotions and feelings using online medium. In this paper, a review is done for how they are emotional interacting in social media networks, and then using these characteristics to discriminate friends online.

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The goal is display content online which is extracted from emotional texts they post in online social media networks. The concern is to express the text which matches to the writers emotions. For this purpose text mining techniques are performed on comments grasped from a social media network. The process comprises a model for database schemas, data collection, processing and mining steps. The main point of consideration is the use of informal language of online social networks, before performing text mining techniques.

The technique assumes is unsupervised k-means clustering algorithm is mainly used. To determine subjectivity of text and predicting friends in social media high accuracy is expressed. Keywords Text Representation, Text Mining, Online Social Networking, emotion mining, NLP, Machine Learning.

1. Introduction As with the increase of online users the increase in trends of use of online platform like social media such as twitter, facebook, linkedin, hike, whatsapp, blogging, online discussions, multimedia sharing as a communicating channel. Social media sites play a very important role in current web applications, as shown in Figure 1. statistics of Statista, the top 10 online social networking sites which is most popular. Fig 1 Social Media interaction in different disciplines includes politics, business, economics, psychology, sociology and other society aspects are shared and discussed massively by online interaction and collective behavior to attract much attentions by writers. To calculate strengthen relationship between two persons based on their online content they share and comments, an application approach is applied. The primary interest is to detect individuals emotions in their text.

To strengthen the relationship great influence to convey emotions is to be assessed. To distinguish close friends from association on the social media networks this technique can be applied. Sentence structure and informal language use to describe emotions apart from formal written language in this environments is major drawback to distinct the text, in any type of text mining these factors should be taken into consideration. 2. LITERATURE REVIEW 1 2.1 Emotions Considering Hereditary it is the human nature that everyone has their own emotions. It has been also found that expression of particular emotion is identical to every human being. These emotions are sometime persistent to long and result in mood.

A persons certain emotions vary or combinable result into mood. Basically the human emotions can be classified into two categorize basic and complex. Here basic emotions are joy, anger, surprise, sadness, fear and disgust. On the other side complex emotions are combination of two or more basic emotions that are experienced by a person at an instance. Emotions Mining can be done from texts or comments posted or shared online in social media network by an individual. The writers feelings and emotional state or some time even impassive or feeling less text are reflected by their texts and shared media is to mined.

As there are universal expression and emotions but there is huge difference between culture and between individuals in the way they express their emotions. In general, men dont express and share their emotions online then comparing to women and such an observation was also verified in online social networking. In addition, behavior is also an essential factor persuade emotions. Also, Social factors had impact on emotions where the emotions are not at all limited to a persons personal feelings but it is subjective to society, a persons tactical goals and experiences. 2.

2 Emotion Mining Emotion mining can be classified into three main categories which depend on the intention for mining emotions. The first grouping endeavor s extracting the valence of the text, which is associated with the text, has positive or negative emotions indications. The second category endeavors to identify whether the text is accurate (i.e. intention) or subjective, the purpose is to find whether text is emotionally rich or emotion less.

The third category endeavors on recognizing potency or arousal in the text along with emotions. Generally, to automate emotion mining many techniques have been used with some exceptions. These can be classified into four categories Keyword Spotting It is based on a lexicon combining words that have emotional connotations. Predicting the emotions of the writer by grasping sentimental words from the text is done in these techniques. These techniques are trendy because of their simplicity and economical advantage. Particular emotions are explicated and replicates clearly, for an instance happy replicates happiness and scared replicates fear. Lexical similarity events These techniques are a bit more advanced than keyword spotting where they assign for each word a probabilistic similarity for a certain emotion. For example, the word success has an 80 probability of replicating a positive event.

Similar to keyword spotting, lexical similarity techniques perform poorly when observing complex sentence structures like This was not a success at all Statistical NLP techniques These techniques utilize machine learning algorithms to learn words lexical similarity and frequent words reoccurrence. Regretfully, the results has no predictive values unless a larger quantity of text is used, especially in social media network sphere where the language used has no proper composition and statistical rules which is harder to learn. Thus it might not be feasible to simply use these techniques because openly available data are hard to find. Hand-crafted Models Deep understandings and analysis of particular text in order to mine emotions is used in these models. These models have complex systems and their findings are difficult to simplify to other texts. 2.

3 Language in Online Social Networks Accounting must be done for specificity of texts in online social networking sites. Though it is common in online social networking sites for writers to use an informal and less structured language for communicating with their friends. Some features of such uses in online language are presented below Misspelling intentionally, repeating a letter in same word, (e.g. hiiiiiii). Interjections and lexical substitution for vocalizations (e.g. mwah indicating a kiss or hmmmm).

Grammatical indication such as the use of capital letters and the unnecessary use of punctuation (e.g. recurrence). Social Acronyms Acronyms of popular terms used in online conversation. For instance TC denotes the expression take care.

Emojis Visual characters in order to form symbolic facial pictures conveying emotions. For instance ) ( indicates joy and ( ( indicates sadness. Apart from informal language, sentences with lack of suitable sentence structure and words are also misspelled. Also, use of non-english use of regional languages translations into English alphabets is widely used in common use of hindi language is widely used. (i.e. thik hai).

3. Expected Architecture 1 The agenda shown in Figure 2 is classified into six steps Raw Data Collection The collection of exercise data in step 1 is prepared by creating of social network application communicating with social network API. This application stores user information into special database whose schema is discussed below which is retrieved from relevant user. The owners of the data must be aware about the purpose of the data collected and must unambiguously give permission for the use of their data. The UserInfo table includes the user personal information that the user has mentioned on his profile. The uid is assigned to each user as unique identity and through which the method allow to locate the user all over the tables. All the tuples for friend list in user profile is contained in Friends Table. The Posts and comments Table contain the text which is to be mined for emotions.

Messages posted on the users page are stored in Posts table. Where every post is allocated with a unique number pid, a Source_id (the id of the user who initiated the post), a Target_id (the user id of target) and the content of the message along with the timestamp. All the responses to the users post are stored in comments table. Table fields are Parent_id (id of the post), the From_id for the user who has posted the comment, the time, the content of the text and cid which is for comments unique id. The raw data is then collected and stored into tables. Other features from the raw data which will be computed along with is also stored in the database. Dictionary Development Posts in online social media networks are often written in an casual and non-english language.

For such conditions, we divide them in three types of dictionary in step 2. Firstly we prepare a dictionary of mostly used social acronyms in online chatting and in online social networking sites. It is not feasible to cover all possible social acronyms, thus the dictionary is in no way comprehensive. In fact, social acronyms are randomly created and implemented to small group, and in general online community only few of these acronyms are spread.

Some of the popular acronyms are illustrated in below table Table 1 Social AcronymsAcronymSignificanceASAP NPAs Soon As Possible No ProblemA dictionary has been developed for emojis and for interjection. The dictionary is not common but they cover a huge entitlement of what is used generally in online network. Emojis and interjections examples are illustrated in below tables. Table 2 EmojisTable 3 InterjectionsEmojisSignificanceInterjections) -) ( -( – -x P -PSmiling Sad Kissing Jokingnahi, nono hi, hey oh wowEmojis and acronym are associated to the actual words they represent, and accordingly are given a subjectivity weight. Feature generation To classify and review the subjectivity of the text, numerous features need to be examined.

This is done in step 3. These are grouped into three categories The first grouping is based on SentiWordNet, a sentimental dictionary. Features in this category include the number of sentimental words, the average subjectivity determination of affective words, etc The second grouping is based on intentional misspelling fault and grammatical indication such as punctuation and capitalized letters. Features in this grouping consist of the number of punctuation characters, number of capitalized letters, average number of repeated letters when letters are repeated consecutively at least three times, etc..

. The third grouping is based on emojis, interjections and social acronyms. Features representing this grouping include number of emotional weight of emojis, interjections, etc Data preprocessing This step is applied on extorted features. It compromises with eradicating excess characteristic, discretization by clustering and normalization using Min-Max. First, aspect selection is performed based on a relationship determination to remove unnecessary attribute. Nine attributes is derived. Table 4 List of AttributesAttributeCount of punctuation marks Count of capitalized letters Count of exclamation Count of sentimental words Common subjectivity measure of sentimental words Count of repeated letters when letters are repeated consecutively at least three times Count of social acronyms Count of emoticons Common ranking of emoticonsSecond, the values of the characteristic were continuous, and desired to be marked to distinct values. The marking to distinct fields seemed sensitive as the goal is to realize whether the text is subjective or not and known, for example the irrelevant number of punctuation marks, whereas the writer has used many or few punctuation marks for denoting some sort of subjectivity.

Therefore, discretization was done by clustering. The k-mean algorithm was performed on each attribute with k3 or k4. The values of the attributes were swapped respectively by the centroid of the cluster they belong to. Finally, the values in each element were normalized using Min-Max normalization and map them to the range 01. Create text subjectivity training model This step produces a representation using k-means clustering algorithm with k3 to classify texts into three subjectivity levels neutral, moderately subjective and subjective.

The output of the model is the three centroids of the clusters. Classification of Text subjectivity This step employ the centroids produced in the previous steps and utilize the k-nearest neighbor algorithm with k1 to categorize all comments into one of three subjectivity levels. This fragment test the capability of the model to identify three different classes of texts, based on the subjectivity of the writer. Classifying Friendship This step produces SVM preparation representation and then uses it to calculate tie strength between online friends based exclusively on the subjectivity of the texts they share online.

The sorting is done by first categorizing the subjectivity of the texts exchanged which is performed in step 6 and then taking an average determination. Online friends are categorized into two categories close friends and acquaintances. 4. Conclusions Since past decades, there is tremendously increase in the interest as a mandatory for life of interaction at social media networks. Peoples daily activities have huge amount of data representation, whatever feel they right now or their feedback on any event or product or anything else. Information can be mined from these data for examine variety area of domains.

Opinion mining, sentiment analysis or emotion mining is a technology intention to collect writers response towards trending topics or any event. In this paper a discussion on emotions mining techniques for text in online social network is done. A study of friends relationship and emotions expression in online social networks by dealing with the specific nature of these sites and the nature of the language used. The intention was to grab whether the writer expressed their feelings and emotions in their writings. Emotions analysis and generation has vast application domains including human to machine interaction, customer dealings, natural text-to-speech systems, information retrieval and in fictional and social analysis.

As there are limited resources for emotion exists, that also for English language. Though great accomplishment have received, critical challenges requires more attention by large number of researchers. Accuracy, Big Data, Visualization and real time are some of the challenges faced in the domain of emotion mining in online social networking. References Mohamed Yassine and Hazem Hajj, A Framework for Emotion Mining from Text in Online Social Networks, 2010 IEEE International Conference on Data Mining Workshops, 13-13 Dec. 2010. Sanjeev Dhawan, Kulvinder Singh and Deepika Sehrawat, Emotion Mining Techniques in Social Networking Sites.

International Journal of Information Computation Technology, ISSN 0974-2239 Volume 4, Number 12 (2014), pp. 1145-1153. Hany Mohamed, Ayman Ezzat and Mostafa Sami, The Road to Emotion Mining in Social Network. International Journal of Computer Applications (0975 8887), Volume 123 No.18, August 2015. Joo Filipe Figueiredo Pereira, Social Media Text Processing and Semantic Analysis for Smart Cities, Cornell University, Thesis Submitted on 11 Sep 2017.

Xia Hu and Huan Liu, TEXT ANALYTICS IN SOCIAL MEDIA. Mining Text Data, pp 385-414. Biographical notes U.R.Rathor is pursuing M.

E. in Computer Science from HJD Institute of Technical Education Research, Gajod, At. Kera, Kutch, Gujarat, India.

H.D.Chande has received M.

E. in Computer Science. He is Associate Professor – Department of Computer Science Engineering at HJD Institute of Technical Education Research, Gajod, At. Kera, Kutch, Gujarat, India. PAGE National Conference on Emerging Research Trend in Science and Technology HJD Institute of Technical Education Research, At. Kera, Kutch, Gujarat, India.

PAGE PAGE MERGEFORMAT 3 Page International Journal of Darshan Institute on Engineering Research and Emerging Technology ISSN (Print) 2320- 7590 HYPERLINK http//www.ijdieret.in www.

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