Yahoo! for Amazon: Sentiment Extraction from Small Talk on the Web | Semantic Scholar (2024)

A methodology is developed by using association rule to select seed words and introducing new outside resources to improve the traditional PMI performance and the result shows that this methodology is effective.

This paper uses artificial intelligence text analysis methods to construct indices of economic sentiment from a database of 47,000 articles from the Financial Times, finding the FOMC is found to respond strongly even to uninformative components of newspaper sentiment.

Results indicate that correlations between the sentiment in the news and the S&P index are strong for five of the seven years analyzed.

This paper presents a family of trading strategies, and uses this application to re-examine some of the tacit assumptions behind how sentiment analyzers are generally evaluated, in spite of the contexts of their application.

Examination of a popular stock message board finds slight daily predictability using supervised learning algorithms when combining daily sentiment with historical price information and questions if the existence of dishonest posters are capitalizing on the popularity of the boards by writing sentiment in line with their trading goals as a means of influencing others, and therefore undermining the purpose of the board.

The findings show that through the Fama–MacBeth regression method, social media–based sentiment measures can be used as risk factors in an asset pricing framework and these sentiment measures have predictive capability when used as features in a machine learning framework.

An overview of common approaches to this topic is given and the content generated by the financial social network Seekingalpha.com is analyzed, finding that a large proportion of users’ attention is focused on only a few stocks.

An overview of common approaches to this topic is given and the content generated by the financial social network Seekingalpha.com is analyzed, finding that a large proportion of users’ attention is focused on only a few stocks.

Results show that an increase in the sentiment index predicts a positive stock return and reduction in its volatility, and evidence demonstrates that a higher sentiment index leads to an appreciation in the KRW against the USD and the lower exchange rate volatility.

...

...

It is found that stock messages posted on Yahooe Finance and Raging Bull help predict market volatility and disagreement among the posted messages is associated with increased trading volume.

It is found that online conversations may offer an easy and cost-effective opportunity to measure word of mouth and it is shown that a measure of the dispersion of conversations across communities has explanatory power in a dynamic model of TV ratings.

This work has developed a text classifier that misclassified only 13% of the documents in the well-known Reuters benchmark; this was comparable to the best results ever obtained and its technique also adapts gracefully to the fraction of neighboring documents having known topics.

This work proposes an approach that utilizes the hierarchical topic structure to decompose the classification task into a set of simpler problems, one at each node in the classification tree, which can be solved accurately by focusing only on a very small set of features, those relevant to the task at hand.

A model where an altruistic sender, who may or may not be informed, broadcasts one of a finite set of messages to rational receivers shows that overconfidence can improve informativeness when broadcasting is costly.

An automatic system that starts with a small sample of the corpus in which topics have been assigned by hand, and then updates the database with new documents as the corpus grows, assigning topics to these new documents with high speed and accuracy is described.

...

...