• DocumentCode
    3026351
  • Title

    A framework for informal language: Opinion mining

  • Author

    Arora, Monika ; Kansal, Vineet

  • Author_Institution
    BPIT, Delhi, India
  • fYear
    2015
  • fDate
    15-16 May 2015
  • Firstpage
    41
  • Lastpage
    45
  • Abstract
    Blog, Tweets, or any social networking sites are popularly used weblogs where consumers can broadcast their opinions. It has become a common practise among the users to express their feedback about any product or service. This information in-turn has now become a significant component of the global information. The information provided help´s any organization to keep track of the user´s opinion about the product or the service that they are providing and helps the company to find their product popularity and its rating in contrast to the other competitors. This information is important for any organization to improve their market share. Thus, these opinion rich resources need to be mined so that one can extract useful information out of it. The information written by the users is in the unstructured form. The techniques are then applied to extract the opinion or the sentiments about the product. The mining of such unstructured data gives rise to new challenges that need to be focused. In this paper, we have proposed a framework to mine the opinions expressed in form of unstructured data. The unstructured data is generally written in the abbreviated form and might not be syntactically correct. Our proposed framework will resolve such challenges which arise due to the commonly used informal language and to handle this we have introduced a new dictionary namely Slang. This dictionary contains the new commonly used abbreviations with their correct matched words and grows with time for every new abbreviated word.
  • Keywords
    data mining; dictionaries; natural language processing; social networking (online); Slang; Weblogs; blog; dictionary; global information; informal language; information extraction; opinion extraction; opinion mining; product popularity; sentiment extraction; social networking sites; tweets; unstructured data; Blogs; Data mining; Dictionaries; Feature extraction; Sentiment analysis; Tagging; Watches; Opinion Mining; Sentiment Analysis; Text Mining; Unstructured Data;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computing, Communication & Automation (ICCCA), 2015 International Conference on
  • Conference_Location
    Noida
  • Print_ISBN
    978-1-4799-8889-1
  • Type

    conf

  • DOI
    10.1109/CCAA.2015.7148368
  • Filename
    7148368