i2 introduces a breakthrough in Sentiment Analysis that offers a more thorough approach compared to most current approaches that focus primarily on polarity.
Sentiment Analysis has made strides in Natural Language Processing (NLP) but its traditional focus on a single dimension such as polarity has limitations. This new method amalgamates not only polarity but also aspect, mood, and intensity dimensions into a unified sentiment metric.
Described in a recent paper and published in the International Journal of Computer Applications (Volume 185 – No. 27), this breakthrough offers a more thorough approach compared with most commercial approaches.
What is Sentiment Analysis?
The process of computationally identifying and categorizing opinions expressed in a piece of text, especially in order to determine whether the writer's attitude towards the subject is positive, negative, or neutral.
Its effectiveness is highlighted in the following two case studies (taken from the paper).
Consumer Financial Protection Bureau
The first case study employed the new sentiment analysis metric to analyse complaints submitted to the U.S. Consumer Financial Protection Bureau.
It revealed that complaints with higher sentiment scores tended to yield more favorable outcomes for consumers. This insight can therefore be used by businesses to reshape how they handle consumer feedback, prompting them to consider sentiment as a factor in decision-making.
Harnessing sentiment for business insights
This could be useful, for example, to companies whose business is sensitive to consumer feedback. Companies adopting i2's approach could gain nuanced insights into consumer sentiment, offering the potential for tangible financial benefits and better consumer relations.
Decoding Donald Trump's Tweets
The second case study delved into social media by analyzing tweets from former U.S. president Donald Trump. The new sentiment analysis metric successfully differentiated between tweets composed by Trump himself and those he retweeted. Surprisingly, retweeted messages generally carried a more negative sentiment than Trump's original tweets.
Identifying authorship through sentiment
This case study showcases how the sentiment metric effectively discerns between tweets authored by President Trump and those he retweeted and underscores its value in scenarios requiring authorship identification.
A New Era for Sentiment Analysis
The paper by Kemp Williams, Gregory Roberts, and Jason James propels sentiment analysis into a new era.
By encompassing multiple dimensions, this comprehensive sentiment metric provides a richer understanding of sentiment, as demonstrated in business decision-making and authorship attribution.
Sentiment Analysis with i2
i2's approach introduces a more holistic perspective for sentiment analysis, making it an invaluable tool in various domains.
All our tools offer data integration and advanced analytical capabilities to aid data analysts in understanding relationships and uncovering hidden connections.
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