Research
Sentiment Polarity Identification of Social Media content using Artificial Neural Networks
Sentiment of people about consumer goods and government policies for decision making is normally collected through feedback forms, surveys etc. The social network sites and micro blogging sites are considered a very good source of information nowadays because people share and discuss their opinions about a certain topic freely. With the increased use of technology and social media, people proactively express their opinion through social media sites like Twitter, Facebook, Instagram etc. A social media sentiment analysis can help companies to understand how people feel about their products. On the other hand, extracting the sentiment from social media text is a challenging task due to the complexity of natural language processing of social media language. Often these messages reflect the emotion, opinion and sentiment of the public through a mix of text, image, emoticons etc. These statements are often called electronic Word of Mouth (eWOM) and are much prevalent in business and service industry to enable customers to share their point of view.
Entity Matching for Digital World: A Modern Approach using Artificial Intelligence and Machine Learning
Entity matching is the field of research solving the problem of identifying similar records which refer to the same real-world entity. In today’s digital world, business organizations deal with large amount of data like customers, vendors, manufacturers, etc. Entities are spread across various data sources and failure to correlate two records as one entity can lead to confusion. Relationships and patterns would be missed. Aggregations and calculations won’t make any sense. It is a significant data integration effort that often arises when data originate from different sources. In such scenarios, we understand the situation by linking records and then track entities from a person to a product, etc. There is appreciable value in integrating the data silos across various industries.
