Showing papers on "Sentiment analysis published in 2001"

Yahoo! For Amazon: Sentiment Parsing from Small Talk on the Web

Abstract: The internet has made it feasible to tap a continuous stream of public sentiment from the world wide web, quite literally permitting one to "feel the pulse" of any issue under consideration. We present a methodology for real time sentiment extraction in the domain of finance. With the advent of the web, there has been a sharp increase in the influence of individuals on the stock market via web-based trading and the posting of sentiment to stock message boards. While it is importantto capture this "sentiment" of small investors, as yet, no index of sentiment has been compiled. This paper comprises (a) a technology for extracting small investor sentiment from web sources to create an index, and (b) illustrative applications of the methodology. We make use of computerized natural language and statistical algorithms for the automated classification of messages posted on the web. We design a suite of classification algorithms, each of different theoretical content, with a view to characterizing the sentiment of any single posting to a message board. The use of multiple methods allows imposition of voting rules in the classification process. It also enables elimination of "fuzzy" messages which are better off uninterpreted. A majority rule across algorithms vastly improves classification accuracy, but also leads to a natural increase in the number of messages classified as "fuzzy". The classifier achieves an accuracy of 62% (versus a random classification accuracy of 33%), and compares favorably against human agreement on message classification, which was 72%. The technology is computationally efficient, allowing the access and interpretations of thousands of messages within minutes. Our illustrative applications show evidence of a strong link between market movements and sentiment. Based on approximately 25,000 messages for the last quarter of 2000, we found evidence that sentiment is based on stock movements.

Hybrid Natural Language Processing in a Customer-Care Environment

Abstract: CyMON is an industrial platform for one-to-one customer care applications. It compromises facilities to implement user-friendly websites with an emotional, learning interface. The CyMON Natural Language Processing Engine can understand written input and react accordingly. It is based on hybrid linguistic and statistical algorithms to analyze natural language input. Each sentence is assigned an appropriate contextualized meaning representation. This paper describes the results of an evaluation of basic pattern matching techniques and argues in favor of their augmentation with more sophisticated models taken from linguistics and statistics. The natural language processing techniques applied in CyMON are described in detail. An overview of related work is provided.