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2018 International Conference on Soft-computing and Network Security (ICSNS)

Tweet Analysis Based On Distinct Opinion of Social Media Users’

S.GEETHA1, VISHNU KUMAR KALIAPPAN2

1PG Scholar /CSE Department /KPR Institute of Engineering and Technology

2 Faculty/CSE Department /KPR Institute of Engineering and Technology

[email protected], [email protected]

ABSTRACT

The state of mind gets expressed via Emojis’ and Text Messages for the huge population. Microblogging and social networking sites emerged as a popular communication channels among the internet users. Supervised text classifiers are used for sentimental analysis in both general and specific emotions detection with more accuracy. The main objective is to include intensity for predicting the different texts formats from twitter, by considering a text context associated with the emoticons and punctuations. The novel Future Prediction Architecture Based On Efficient Classification (FPAEC) is designed with various classification algorithms such as, Fisher’s Linear Discriminant Classifier (FLDC), Support Vector Machine (SVM), Naïve Bayes Classifier (NBC) and Artificial Neural Network (ANN) Algorithm along with the BIRCH (Balanced Iterative Reducing and Clustering using Hierarchies) clustering algorithm. The priliminary stage is to analyze the distinct classification algorithm’s efficiency, during the prediction process. Later, the classified data will be clustered to extract the required information from the trained data set using BIRCH method, for predicting the future. Finally, the perfomance of text analysis can get improved by using efficient classification algorithm. Keywords: Text Classifiers, Emoticons, Twitter, Social Networking.

I. INTRODUCTION

The text mining is a process of deriving high quality information from text[1]. High quality information is typically derived through the devising of patterns and trends through statistical pattern learning. A search using text mining helps to identify facts, relatonships and assertions that remain in mass of big data. The actual purpose of networking, socialization and personalization has been completely changed over the past few years due to rise of social media among common people. The sentiment of people can be expressed to others while communicating with internet based social media. One of the most popular micro-blogging social media platforms in recent years is Twitter. Various

analysis tools are available for the collection of twitter data. The text mining process will help to discover the right document and extract the knowledge automatically from various unstructured data content. The actual sentiment conveyed on the particular tweet can be analysed exactly when dealing with group of words context including the emoticons. Twitter data is collected via the Twitter Streaming API which is a quantitative approach. A deep insight can be gained with the public views understanding.

The unstructured data requires a lion’s share in digital space (i.e.) about 80% of volume. Computational study of people’s opinion, sentiments, evaluation, emotions and attitudes are performed by sentimental analysis. Social Media data growth rate coincides with the growing importance of sentiment analyses. The new tool helps to “train” for sentiment analysis in the upgraded platform which includes by combining the company’s natural language processing technology relatively with a straight forward machine language system.

Twitter creates an outstanding opportunity in the perspective of software engineering to track and monitor the large population end-user’s opinion on various topics by establishing a unique environment. The opinions are expressed about distinct products, services, events, political parties, organizations, etc. as an enormous amount of data from users. Due to informal and unstructured data format of Twitter, there is a difficulty in reacting to the feedback quickly for government or any organizations.

The favourable and non-favourable reaction in text can be determined in the research field of sentiment analysis. The text emotions can discover opinion from the users’ tweets. The major text classification based on two sentiments such as positive () and negative (). The text multidimensional emotions are computed based on the following emotions: anger, fear, anticipation, trust,

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2018 International Conference on Soft-computing and Network Security (ICSNS)

surprise, love, sad, joy, disgust. The positive or negative feelings are conveyed by sentimental analysis techniques, which completely rely on emotion evoking words and opinion lexicons for detecting feelings in the corresponding text.

Figure 1: Has-tag Wheel of Emotions

While monitoring the public opinion and evaluating the former product design during the product feedback monitoring, there are lots of information need to get located with apparent emotiveness.

The rest of this paper sections are organized with: Literature Survey in Section II. The Section III contains related work. Current proposed system of this paper is represented in Section IV. The Section V contains the module description and its Conclusion is presented in Section VI. Future enhancement is given at Section VII.

II. LITERATURE SURVEY

From Text Mining for Sentiment Analysis of the Twitter Data [2], the precious information source is obtained from the text messages collection to understand the actual mind-set of the large population. Opinion analysis has been performed on iPhone and Microsoft based on the tweets. Weka1 software with a positive and negative word set used for the data mining process to compare the word set obtained from the twitter. The list of features required to represent the tweets for assigning sentiment label while generating the Classifier using data mining tools. The collection of word frequency distribution is normal, which can be indicated by the analysing process.

The main two factors that derive the sentiment values are, The Positive and Negative word frequency count and existence of a Positive and Negative Emoticon.

Some of the methodologies applied for Twitter data’s sentiment classification in the process of text mining are: Data Collection, Data Pre-processing, Feature Determination and Sentiment Labelling.

In-order to represent and also to label the tweets for training data, list of sentiment words are used along with the emoticons. The document filtering and document indexing techniques are integrated here for developing an effective approach for tweet analysis in the process of text mining.

From [3] tweet sentiment analysis models are generated by machine learning strategies, which do not generalize across multiple languages. Cross-language sentiment analysis usually performed through machine translation approaches that translate a given source language into the target language of choice. Learning in neural networks is accomplished by network connection weight changes while a set of input instances is repeatedly passed through the network. Once trained, an unknown instance passing through the network is classified according to the values seen at the output layer. Neural networks, in which signal flows from input to output (forward direction) are called, feed forward neural networks. Machine translation is expensive and the results that are provided by theses strategies are limited by the quality of the translation that is performed. Deep convolutional neural networks(CNN) with character-level embeddings made to own for pointing to the proper polarity of tweets, that may be written in distinct (or multiple) languages. A single layer neural network can solve linearly separable problems only.

In [4], the data has been collected from the Twitter and Microblogs to pre-process it for analysing and visualizing given data to do sentiment analysis and text mining by using the open source tools. Customers’ view can be understood from the comparative value change on their products and its services, to provide the future marketing strategies and decision making policies. The business performance can be monitored based on the perspective of customer survey report.

The uncovered knowledge in the inter-relationships pattern can be detected based on data mining algorithms like clustering, classification and association rules for discovering the text pattern of new information’s

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2018 International Conference on Soft-computing and Network Security (ICSNS)

relationships in the textual sources. These patterns can be visualized using the word-cloud or tag cloud at the end of the text mining process.

In-order to perform mining in the Twitter Microblogs, methodologies used here are: Data Access (keyword search using Twitter package), Data Cleaning (get the text and clean data using additional text mining package), Data Analysis (sentiment analysis performed for the structure representation of tweets using lexicon approach along with scoring function to assign tweet’s score) and Visualization (frequency of words for customer tweets can be shown by word-cloud package and bar plots). The consumer’s opinions are tracked and analysed from social media data by utilizing the products marketing plans and its business intelligence.

From [5], any type of computer-based tweet analysis should address atleast two specific issues: one, many different electronic devices like cell phones and tablets are used by users to post a tweet by developing their own specific culture of vocabulary leads to increase misspellings frequency and different slang in tweets. Next, messages are posted on a variety of topics are tailored to specific topics, unlike blogs, news, and other sites, by Twitter users.

In [6], the Feature set gets enriched by Emoticons available in the tweets. The bag-of-words and the feature hashing information provide complement to the number of positive and negative emotions. Moreover, number of positive and negative lexicons is computed in each message.

From[7], visualizing and understanding character level networks has to be reasonable and interpretable even when dealing with sentences whose words come from multiple distinct languages. The multilingual sentiment analysis has the most common approach called Cross- Language Sentiment Classification (CLSC), which focuses on the use of machine translation techniques inorder to translate a given source language to the desired target language. The polarity gets identified within the sentences with multiple languages together. It has a reasonable and interpretable to deal with words come from multiple languages. From [8] WEAN, a word emotion computation algorithm used to obtain the initial words emotion, which are further refined through the standard emotion thesaurus. With the words emotion every sentence's sentiment can get computed. The news event’s sentiment computing task can be splitted into two procedures: word emotion

computation through word emotion association network and word emotion refinement through standard sentiment thesaurus. After constructing WEAN, compute word emotion of ek for the different scale and intension of word circumstance that can affect word emotion. The larger of scale and stronger of the intension results the more intense of the word emotion. The sentiment classification has its effect on the various pre-processing methods in [9], includes removing URLs, replacing negation, reverting repeated letters, removing stop words, removing numbers and expanding acronyms. The two feature models and four classifiers used to identify tweet sentiment polarity on Twitter datasets. The performance of sentiment classification improves after expanding acronyms and replacing negation, but barely changes when removing URLs and removing stop words or numbers. In [10], multidimensional structure challenges address a preliminary analysis aimed at detecting and interpreting emotions present in software-relevant tweets. The most effective techniques in detecting emotions and collective mood states in software-relevant tweets get identified and then investigated based on emotions that are correlated with specific software-related events. In [11] a system to process short text and filter them precisely based on the semantics of information contained in it. The system analyzes tweets to find interesting result and integrates sentiments with each individual keyword. Feature extraction is applied to find keywords and sentiments from tweets get expressed by user about the keywords. The semantic based filtering is performed for specific category using seed list (domain specific) to decreases the information loss. The information gain gets maximized by filtering on keywords, verbs, entities and their synonyms extracted from tweet. The improvements in classification results can be quantified by an analytical framework due to the combination of multiple models. Training data learners constructs a set of learners and combines them in the ensemble methods. The unseen data can gets classified by classifiers and are known as a target dataset. The status updates with hashtags helps to identify the reduired content easily from the large data-set.

From [12], the automatic conversion of text to emotion is a challenge as it minimizes the misunderstanding by conveying the internal state of the users. The framework get divided into two modules, namely Training Module

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2018 International Conference on Soft-computing and Network Security (ICSNS)

and Emotion Extraction Module. In Training Module, Exploratory Business Intelligence (BI) approach includes the external data besides internal corporate data. Extraction phase of the Training Module extract training data from the manual TEA database from user feedback, suggestions and the data from web-based social media such as Twitter.

III. RELATED WORK

Various methodologies and algorithms are proposed to perform the text mining in social media based on distinct opinions. The table represents the efficiency and performance of the classification and clustering algorithms based on the survey.

S.NO ALGORITHM PURPOSE E P A

1 Navie Bayes Classify M H H

2 Support Vector Machine

Classify (T+E)

H M M

3 Fisher’s Linear Discriminant Classifier

Classify L H H

4 Neural Network Classify M M H

5 Convolutional Neural Network

Classify (T+E)

M H M

6 Balanced Iterative Reducing and Clustering using Hierarchies

Clustering

Table 1: Algorithms Comparison

In the above table 1, the algorithms quality gets analyzed based on the efficiency(E), accuracy(A) and performance(P) factors representing as Low(L), Medium(M) and High(H) values.

IV. PROPOSED SYSTEM

While computing the text emotion with a particular word can cause performance problem during the analysis. The classification of text is performed without considering the efficient classification algorithm, which will affect the quality factors of the text analysis.

A novel architecture, called FPAEC is proposed for word context associated with the emoticons and punctuations to predict the intensity for the piece of different texts, instead of using the particular word and emotion alone. The complete word context with emotion is considered here for exact analysis of the tweet dealing. Collect the bag of words from twitter and sent to each classifiers separately to find the classification efficiency of each algorithm which is specified in figure 2. Secondly, combine the classifiers using clustering method to improve the accuracy and speed of the analysis. The result of the each classifier after clustering is summed up to obtain the exact value for the feature extraction and future prediction. The processed data will get stored in the refined database for the further analysis in future with an improved performance of the model.

Figure 2: FPAEC Architecture

The comparative study is made for the different classification algorithms to predict the classification algorithms quality factors in the applications. Finally the classified data is clustered to extract the required information from the trained data set.

NEET problem is considered as an application here for the future prediction. The amount of natural language text

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2018 International Conference on Soft-computing and Network Security (ICSNS)

that is available in electronic form is truly staggering, and is increasing every day and access the information in that text. The prediction of analysis helps to know the neccessity of it in near future. The complete analysis will provide a efficient visualization of the NEET problem in the society.

V. MODULE DESCRIPTION

Collection of Data: The process of gathering and

measuring information on variables of interest, in an

established systematic fashion that enables one to answer

stated research questions, test hypotheses, and evaluate

outcomes.

Text Pre-Processing: The input document is processed

for removing redundancies, inconsistencies, separate

words, stemming and documents are prepared for next

step.

Comparison Of Classification Algorithms: The

classification algorithms such as Support Vector Machine,

Fisher’s Linear Discriminant Classifier, Naïve Bayes

Classifier and Artificial Neural Network algorithm are

used to perform the classification of text. The quality

factors such as efficiency, speed and accuracy are

analyzed among these classification algorithms and

clustering algorithm to improve the overall

implementation of tweet analysis for the real time

applications.

Clustering: The BIRCH clustering algorithm is an

efficient and scalable method, which has ability to

incrementally and dynamically cluster incoming, multi-

dimensional metric data point in an attempt to produce the

best quality clustering for a given set of resources. Its

performance extensively in terms of memory

requirements, running time, clustering quality, stability

and scalability.

Information Extraction: Overall analysis is performed

to predict the future of the analysis with the help of values

evaluated from the clustered data sets. At the final stage

of the analysis, the future of the analyzed specific area

can be predicted with more accuracy than the previous

analysis.

VI. CONCLUSION

Enormous amount of data are available in twitter for the opinion analysis to share and exchange the information. On-going research on mining tweets is developed to classify and analysis unstructured twitter data based on their extracted features with emotions. The twitter data can be classified accurately into three classes: positive, negative and neutral for proposed sentiment classifier along with extracted features from word context emotions. The performance efficiency among the classifiers are analyzed and represented using graph to use that result for predicting the future. Machine translation, paired datasets, or word-embedding’s are not required in this approach to perform the proposed task. The value of emotion included to particular information gets described clearly in our proposed paper.

VII. FUTURE ENHANCEMENT

In future, neutral tweets are studied with the datasets which are enriched with analogue domain and also with different features extraction. Automatic interpreting of various emotions in different application domains need to develop based on public’s moods. Towards this direction, the more effort will be devoted in future.

REFERENCE

[1]www.irphouse.com

[2] Text Mining for Sentiment Analysis of Twitter Data, by Shruti Wakade, Chandra Shekar, Kathy J. Liszka and Chien- Chung Chan

[3] Sentiment Analysis and Text Mining for Social Media Microblogs using Open Source Tools: An Empirical Study, by Eman M.G. Younis on Feb 2015

[4] Learning Sentiment-SpecificWord Embedding for Twitter Sentiment Classification, in 2014, by Duyu Tang, Furu Wei, Nan Yang, Ming Zhou, Ting Liu, Bing Qin

[5] Tweet sentiment analysis with classifier ensembles, by Nádia F.F. da Silva a, Eduardo R. Hruschka a, Estevam R. Hruschka in 2014

[6] Mining Tweets for Education Reforms, by Mwana Said Omar, Alexander Njeru, Samiullah Paracha, Muhammad Wannous, Sun Yi in 2017

[7] A Character-based Convolutional Neural Network for Language-Agnostic Twitter Sentiment Analysis, by Jˆonatas Wehrmann, Willian Becker, Henry E. L. Cagnini, and Rodrigo C. Barros in 2017

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2018 International Conference on Soft-computing and Network Security (ICSNS)

[8] Sentiment Computing for the News Event Based on the Social Media Big Data, by DANDAN JIANG, XIANGFENG LUO, JUNYU XUAN, AND ZHENG XU on September 2016

[9] Comparison Research on Text Pre-processing Methods on Twitter Sentiment Analysis, by ZHAO JIANQIANG and GUI XIAOLIN on March 2017.

[10] Analyzing, Classifying, and Interpreting Emotions in Software Users’ Tweets, by Grant Williams∗ and Anas Mahmoud, in 2017

[11] Precise Tweet Classification and Sentiment Analysis, by Rabia Batool, Asad Masood Khattak, Jahanzeb Maqbool and Sungyoung Lee in 2013

[12]An Intelligent Framework for Text-to-Emotion Analyzer, by Nadia Afroz, Mahim-Ul Asad, Lily Dey on December 2015.

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/HRV (Za stvaranje Adobe PDF dokumenata pogodnih za pouzdani prikaz i ispis poslovnih dokumenata koristite ove postavke. Stvoreni PDF dokumenti mogu se otvoriti Acrobat i Adobe Reader 5.0 i kasnijim verzijama.) /HUN <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> /ITA (Utilizzare queste impostazioni per creare documenti Adobe PDF adatti per visualizzare e stampare documenti aziendali in modo affidabile. I documenti PDF creati possono essere aperti con Acrobat e Adobe Reader 5.0 e versioni successive.) /JPN <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> /KOR <FEFFc7740020c124c815c7440020c0acc6a9d558c5ec0020be44c988b2c8c2a40020bb38c11cb97c0020c548c815c801c73cb85c0020bcf4ace00020c778c1c4d558b2940020b3700020ac00c7a50020c801d569d55c002000410064006f0062006500200050004400460020bb38c11cb97c0020c791c131d569b2c8b2e4002e0020c774b807ac8c0020c791c131b41c00200050004400460020bb38c11cb2940020004100630072006f0062006100740020bc0f002000410064006f00620065002000520065006100640065007200200035002e00300020c774c0c1c5d0c11c0020c5f40020c2180020c788c2b5b2c8b2e4002e> /NLD (Gebruik deze instellingen om Adobe PDF-documenten te maken waarmee zakelijke documenten betrouwbaar kunnen worden weergegeven en afgedrukt. De gemaakte PDF-documenten kunnen worden geopend met Acrobat en Adobe Reader 5.0 en hoger.) /NOR <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> /POL 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<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> /ENU (Use these settings to create Adobe PDF documents suitable for reliable viewing and printing of business documents. Created PDF documents can be opened with Acrobat and Adobe Reader 5.0 and later.) >> >> setdistillerparams << /HWResolution [600 600] /PageSize [612.000 792.000] >> setpagedevice