Environmental Scanning for Customer Complaint Identification in Social Media

Social media provides a platform for dissatisfied and frustrated customers to discuss matters of common concerns and share experiences about products and services. While listening to and learning from customer has long been recognized as an important marketing charge, how to identify customer complaints on social media is a nontrivial task. Customer complaint messages are highly distributed on social media, while non-complaint messages are unspecific and topically diverse. It is costly and time consuming to manually label a large number of customer complaint messages (positive examples) and non-complaint messages (negative examples) for training classification systems. Nevertheless, it is relatively easy to obtain large volumes of unlabeled content on social media. In this paper, we propose a partially supervised learning approach to automatically extract high quality positive and negative examples from an unlabeled dataset. The empirical evaluation suggested that the proposed approach generally outperforms the benchmark techniques and exhibits more stable performance

Identification of Consumer Adverse Drug Reaction Messages on Social Media

The prevalence of social media has resulted in spikes of data on the Internet which can have potential use to assist in many aspects of human life. One prospective use of the data is in the development of an early warning system to monitor consumer Adverse Drug Reactions (ADRs). The direct reporting of ADRs by consumers is playing an increasingly important role in the world of pharmacovigilance. Social media provides patients a platform to exchange their experiences regarding the use of certain drugs. However, the messages posted on those social media networks contain both ADR related messages (positive examples) and non-ADR related messages (negative examples). In this paper, we integrate text mining and partially supervised learning methods to automatically extract and classify messages posted on social media networks into positive and negative examples. Our findings can provide managerial insights into how social media analytics can improve not only postmarketing surveillance, but also other problem domains where large quantity of user-generated content is available

A Decision Support System for Market Segmentation - A Neural Networks Approach

Market segmentation refers to the subdividing of a market into distinct subsets of customers where any subset may conceivably be selected as a market target to be reached with a distinct marketing mix [Kotler 1980]. The reason for segmenting a market is that consumers are often numerous, geographically dispersed, and heterogeneous, and therefore seek varying benefits from the products they buy. Consumers within a segment are expected to have homogeneous buying preferences whereas those in different segments tend to behave differently. By properly identifying the benefit segment of a firm\u27s product, the marketing manager can target the sales effort at specific groups of consumers rather than at the total population. The identification of consumer segments is of critical importance for key strategic issues in marketing involving the assessment of a firm\u27s opportunities and threats. The marketing manager must evaluate the potential of the firm\u27s products in the target segment and ultimately select the most promising strategy for the segment. In thisresearch, we introduce a new approach, a neural networks based method, to discover market segments and configure them into meaningful structures. The particular type of neural networks, the Self-Organizing Map networks, can be used as a decision support tool for supporting strategic decisions involving identifying and targeting market segments. The Self-Organizing Map (SOM) network, a variation of neural computing networks, is a categorization network developed by Kohonen. The theory of the SOM network is motivated by the observation of the operation of the brain. This paper presents the technique of SOM and shows how it may be applied as a clustering tool to market segmentation. A computer program for implementing the SOM neural networks is developed and the results will be compared with other clustering approaches. The study demonstrates the potential of using the Self-Organizing Map as the clustering tool for market segmentation

An Improved Web Design to Support Online Investment Decisions

The rise of the Internet opens up new possibilities and creates new challenges for investors. The possibilities include ease of use, cheaper trading costs, and greatly improved access to information. The challenges include information overload and a temptation to overtrade. The present paper discusses how brokerage firms can improve their web site designs in order to meet these challenges and opportunities and to better facilitate the needs of individual investors. Specifically, the paper discusses how an objectoriented information representation system can be used to enable both investor-specific information, such as risktolerance level, investment time horizon, and tax status, and more general information from the financial markets themselves, such as company P/E levels, to be integrated into a consistent web presentation that will facilitate the investor’s making more intelligent investment decisions. Such an information representation system would be structured hierarchically, with the investor-specific information at the top of the hierarchy, driving the application of market-level, then industry-level, and, at the bottom of the hierarchy, company-specific information. Finally, the paper discusses the feasibility of implementing such a system and some of the promises and pitfalls that may arise from its implementation

Managerial Applications of Neural Networks: The Case of Bank Failure Predictions

This Paper introduces a neural-net approach to perform discriminant analysis in business research. A neural net represents a nonlinear discriminant function as a pattern of connections between its processing units. Using bank default data, the neural-net approach is compared with linear classifier, logistic regression, kNN, and ID3. Empirical results show that neural nets is a promising method of evaluating bank conditions in terms of predictive accuracy, adaptability, and robustness. Limitations of using neural nets as a general modeling tool are also discussed