5,369 research outputs found
Context Based Classification of Reviews Using Association Rule Mining, Fuzzy Logics and Ontology
The Internet has facilitated the growth of recommendation system owing to the ease of sharing customer experiences online. It is a challenging task to summarize and streamline the online textual reviews. In this paper, we propose a new framework called Fuzzy based contextual recommendation system. For classification of customer reviews we extract the information from the reviews based on the context given by users. We use text mining techniques to tag the review and extract context. Then we find out the relationship between the contexts from the ontological database. We incorporate fuzzy based semantic analyzer to find the relationship between the review and the context when they are not found therein. The sentence based classification predicts the relevant reviews, whereas the fuzzy based context method predicts the relevant instances among the relevant reviews. Textual analysis is carried out with the combination of association rules and ontology mining. The relationship between review and their context is compared using the semantic analyzer which is based on the fuzzy rules
Context Based Classification of Reviews Using Association Rule Mining, Fuzzy Logics and Ontology
The Internet has facilitated the growth of recommendation system owing to the ease of sharing customer experiences online. It is a challenging task to summarize and streamline the online textual reviews. In this paper, we propose a new framework called Fuzzy based contextual recommendation system. For classification of customer reviews we extract the information from the reviews based on the context given by users. We use text mining techniques to tag the review and extract context. Then we find out the relationship between the contexts from the ontological database. We incorporate fuzzy based semantic analyzer to find the relationship between the review and the context when they are not found therein. The sentence based classification predicts the relevant reviews, whereas the fuzzy based context method predicts the relevant instances among the relevant reviews. Textual analysis is carried out with the combination of association rules and ontology mining. The relationship between review and their context is compared using the semantic analyzer which is based on the fuzzy rules
Emotional Tendency Analysis of Twitter Data Streams
The web now seems to be an alive and dynamic arena in which billions of people across the globe connect, share, publish, and engage in a broad range of everyday activities. Using social media, individuals may connect and communicate with each other at any time and from any location. More than 500 million individuals across the globe post their thoughts and opinions on the internet every day. There is a huge amount of information created from a variety of social media platforms in a variety of formats and languages throughout the globe. Individuals define emotions as powerful feelings directed toward something or someone as a result of internal or external events that have a personal meaning. Emotional recognition in text has several applications in human-computer interface and natural language processing (NLP). Emotion classification has previously been studied using bag-of words classifiers or deep learning methods on static Twitter data. For real-time textual emotion identification, the proposed model combines a mix of keyword-based and learning-based models, as well as a real-time Emotional Tendency Analysi
Schema-Driven Actionable Insight Generation and Smart Recommendation
In natural language generation (NLG), insight mining is seen as a
data-to-text task, where data is mined for interesting patterns and verbalised
into 'insight' statements. An 'over-generate and rank' paradigm is intuitively
used to generate such insights. The multidimensionality and subjectivity of
this process make it challenging. This paper introduces a schema-driven method
to generate actionable insights from data to drive growth and change. It also
introduces a technique to rank the insights to align with user interests based
on their feedback. We show preliminary qualitative results of the insights
generated using our technique and demonstrate its ability to adapt to feedback
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