A new paper published in the Global Journal of Enterprise Information System explores how social media analysis can be applied to break down public opinion on consumer products (such as Apple IPhone or ITunes) by gender and location.
As presented in this paper, NamSor Gender API can be used to break down sentiment by gender. What do Indian women and men think of Apple products in Chennai, Mumbai or Bangalore?
Not presented in the paper, NamSor Origin API can also provide additional insights to ‘fill the gaps’ on location-based analysis : on Twitter, for example, only about ~5% of all tweets are geo-tagged. NamSor can enrich data with the likely country of origin for the Twitter account (India/Andhra Pradesh, India/Assam, Italy, etc.)
This unique capability can be easily integrated into social listening platforms (such as Synthesio, Linfluence, Crimson Hexagon Analytics, Netbase, Sysomos … and the likes) used by the World’s top consumer good companies to monitor their brand reputation. All it takes is an add-on to connect the platform indexing process to NamSor API.
Location based Twitter Opinion Mining using Common-Sense Information
Amita Jain and Minni Jain
Department of Computer Science and Engineering, Ambedkar Institute of Advanced Communication Technologies and Research, Delhi, India
Sentiment analysis research of public information from social networking sites has been increasing immensely in recent years. Data available at social networking sites is one of the most effective and accurate source to identify the public sentiment of any product/service. In this paper, we propose a novel localized opinion mining model based on common sense information extracted from ConceptNet ontology. The proposed methodology allows interpretation and utilization of data extracted from social media site “Twitter” to identify public opinions. This paper includes location specific, male- female specific and concept specific popularities of product. All extracted concepts are used to calculate senti_score and to build a machine learning model that classifies the user opinions as positive or negative.
Keywords: ConceptNet, Natural Language Processing, Sentiment Analysis, SentiWordNet
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