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A FRAMEWORK FOR FINE GRAINED SENTIMENT ANALYSIS ON CODE-MIXED
                              LANGUAGE FOR SOCIAL MEDIA USER BEHAVIOURS




                                                                         2
                                                          1
                                                Anand, Tank ; Pratik, Vanjara
                                               1 Atmiya University – Rajkot, India
                                    2 Shree M. P. Shah Commerce College – Surendranagar, India




                              ABSTRACT
                                                              Extraction  and  comprehension  of  human  dynamics,
           Here,  we  provide  a  framework  that  discovers  sentiments   including actions, trends, attitudes, and emotions, are made
           from social media platforms, assesses, and transforms them   easier by sentiment analysis of subjective data [3]. Through
           into  meaningful  data.  Social  media  is  changing  people's   forums, blogs, wikis, social networks, and other online tools,
           attitudes  and  habits,  which  in  turn  is  influencing  their   millions of people communicate their thoughts and feelings.
           choices.  Attempting  to  keep  an  eye  on  social  networking   Sentiment  Analysis  (SA)  examines  a  user’s  thoughts,
           activity is a useful tool for tracking consumer attitude about   attitudes,  perspectives,  ideas,  beliefs,  remarks,  requests,
           products and firms and gauging loyalty from consumers. The   inquiries,  and  preferences  toward  various  things  such  as
           framework can be the next natural area for branding based   services,  issues,  people,  products,  events,  subjects,
           on the polarities on the internet and social media. We present   organizations,  and  their  characteristics,  based  more  on
           a dynamic solution method for sentiment analysis using the   emotion than on logic in the form of writing. It determines
           classification of interpersonal data sources. To evaluate the   the general mood of the writer for a text, which could include
           caliber of social information services, we also introduce a   speeches, product evaluations, blog entries, online forums,
           brand-new quality model. We utilize public comments, posts   database  sources,  social  media  information,  and  papers.
           through social media as an inspiring case study. Specifically,   Depending on the circumstances, it typically consists of three
           to pinpoint the comments and posts we concentrate on the   components: 1) Opinions or emotions, 2) Subject, 3) Opinion
           spatiotemporal characteristics of the attitudes expressed by   Holder [4].
           social  media  users.  On  datasets  from  the  real  world,
           experiments  are  carried  out.  Our  suggested  model’s   Sentiment  analysis  of  subjective  datasets  facilitates  the
           performance  is  preliminary  demonstrated  by  performance   extraction and understanding of human dynamics, including
           evaluation matrix.                                 behaviors,  patterns,  attitudes,  and  emotions  [5].  A  lot  of
                                                              noise,  or  superfluous  and  useless  material,  is  frequently
            Keywords – Sentiment Analysis, social media, Artificial   present in social Datasets. Furthermore, there are many kinds
               Intelligence, Machine Learning, Natural Language   of social media information services available on the internet.
                               Processing                     On the internet, there are numerous data providers that have
                                                              distinct data characteristics, such as size, quality, and so on.
                          1.  INTRODUCTION                    Thus, every dataset needs a different approach for extracting
                                                              useful information. It takes time to use different tools, and
           Societies use their sentiments to convey their opinions and   the  perspectives  of  the  data  from  social  sensors  are  not
           behaves as  per experience.  Sentiment analysis is  the  term   always consistent. [6].
           used to describe the analysis of these kinds of perspectives.
           Social networks, or social networking sites, like Facebook,   Our study presents a system called "Fine Grained Sentiment
           Twitter,  and  others,  have  become  a  free  community  for   Analysis for Code-Mixed Languages" (FGSACML), which
           dataset [1]. When there is an event, a lot of people use social   gathers sentiments for code-mixed language from multiple
           media services to quickly create and share data i.e. Twitter,   social  media  sites,  analyzes  and  transforms  them  into
           Facebook, LinkedIn, etc. [2]. This data generated by social   meaningful data, and then returns the data in the form of a
           media users has many useful information as metadata: 1) It   polarity. Using different characteristics of the data from the
           has  subjective  information  as  Opinion.  2)  It  includes   social  media  user,  we  analyzed  social  information  like
           spatiotemporal information as Behavior, among others.    Positive, Negative and Neutral. Conventional techniques for
                                                              sentiment evaluation also cover the numerous attributes of
                                                              media  information  like  comments,  posts  and  review.
                                                              Nevertheless, this framework transcends polarities and is not
                                                              just based on sentiment. Two primary methods can be used




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