Multimodal Sentiment Analysis of Instagram Using Cross-media Bag-of-words Model

Instagram, one of social media sharing services has increasing growth of use and popularity during recent years. Photos or videos shared by Instagram users are challenging to be mined and analyzed for some purposes. One type of studies can be applied to Instagram data is sentiment analysis, a field of study that learn and analyze people opinion, sentiment, and (or) evaluation about something. Sentiment analysis applied to Instagram can be used as analytics tool for some business purposes such as user behavior, market intelligence and user evaluation. This research aimed to analyze sentiment contained on Instagrams post by considering two modalities: images and English text on its caption. The Cross-media Bag-of-Words Model (CBM) was applied for analyzing the sentiment contained on Instagrams post. CBM treated text and image features as a unit of vector representation. These cross-media features then classified using logistic regression to predict sentiment values which categorized into three classes: positive, negative and neutral. Simulation results showed that the combination of unigram text features and 56-length images features achieves the highest accuracy. The accuracy achieved is 87.2%. Keywords : Instagram, sentiment analysis, Cross-media Bag-of-Words Model (CBM), logistic regression, classification Bibliography [1] D. Borth, R. Ji, T. Chen, T. Breuel, and S.-F. Chang, “Large-scale visual sentiment ontology and detectors using adjective noun pairs,” in Proceedings of the 21st ACM International Conference on Multimedia, ser. MM '13. New York, NY, USA: ACM, 2013, pp. 223–232. [2] R.-E. Fan, K.-W. Chang, C.-J. Hsieh, X.-R. Wang, and C.-J. Lin, “Liblinear: A library for large linear classification,” J. Mach. Learn. Res., vol. 9, pp. 1871– 1874, Jun. 2008. [3] E. Ferrara, R. Interdonato, and A. 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The necessities for building a model to evaluate Business Intelligence projects- Literature Review

In recent years Business Intelligence (BI) systems have consistently been rated as one of the highest priorities of Information Systems (IS) and business leaders. BI allows firms to apply information for supporting their processes and decisions by combining its capabilities in both of organizational and technical issues. Many of companies are being spent a significant portion of its IT budgets on business intelligence and related technology. Evaluation of BI readiness is vital because it serves two important goals. First, it shows gaps areas where company is not ready to proceed with its BI efforts. By identifying BI readiness gaps, we can avoid wasting time and resources. Second, the evaluation guides us what we need to close the gaps and implement BI with a high probability of success. This paper proposes to present an overview of BI and necessities for evaluation of readiness. Key words: Business intelligence, Evaluation, Success, ReadinessComment: International Journal of Computer Science & Engineering Survey (IJCSES) Vol.3, No.2, April 201

A comparison of theory and practice in market intelligence gathering for Australian micro-businesses and SMEs

Recent government sponsored research has demonstrated that there is a gap between the theory and practice of market intelligence gathering within the Australian micro, small and medium businesses (SMEs). Typically, there is a significant amount of information in literature about 'what needs to be done', however, there is little insight in terms of how market intelligence gathering should occur. This paper provides a novel insight and a comparison between the theory and practices of market intelligence gathering of micro-business and SMEs in Australia and demonstrates an anomoly in so far as typically the literature does not match what actually occurs in practice. A model for market intelligence gathering for micro-businesses and SMEs is also discussed

Binary Particle Swarm Optimization based Biclustering of Web usage Data

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A comparison of theory and practice in market intelligence gathering for Australian micro-businesses and SMEs

Recent government sponsored research has demonstrated that there is a gap between the theory and practice of market intelligence gathering within the Australian micro, small and medium businesses (SMEs). Typically, there is a significant amount of information in literature about 'what needs to be done', however, there is little insight in terms of how market intelligence gathering should occur. This paper provides a novel insight and a comparison between the theory and practices of market intelligence gathering of micro-business and SMEs in Australia and demonstrates an anomoly in so far as typically the literature does not match what actually occurs in practice. A model for market intelligence gathering for micro-businesses and SMEs is also discussed