If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
We propose a domain-independent approach to blog analysis for an assessment of bloggers' sentiments. The generic methodology to detect specific attitudes of human agents is based on representation of conflict scenarios by means of communicative actions (which express sentiments) and causal/argumentative links between their subjects (which may support these sentiments). This methodology is applied to textual data on interhuman interactions including conflicts in opinions of bloggers. Evaluation is conducted in the domain of customer complaints.
This paper presents experiments on subjectivity and polarity classifications of topic-and genre-independent blog posts, making novel use of a linguistic feature, verb class information, and of an online resource, the Wikipedia dictionary, for determining the polarity of adjectives. Each post from a blog is classified as objective, positive, or negative. Our method of determining the polarity of adjectives has an accuracy rate of 90.9%. Accuracy rates of two verb classes demonstrating polarity are 89.3% and 91.2%. Initial classifier results show blog-post accuracies with significant increases above the established baseline classification.
There are numerous applications in which we would like to assess what opinions are being expressed in text documents. Forr example, Martha Stewart's company may have wished to assess the degree of harshness of news articles about her in the recent past. Likewise, a World Bank official may wish to assess the degree of criticism of a proposed dam in Bangladesh. The ability to gauge opinion on a given topic is therefore of critical interest. In this paper, we develop a suite of algorithms which take as input, a set D of documents as well as a topic t, and gauge the degree of opinion expressed about topic t in the set D of documents. Our algorithms can return both a number (larger the number, more positive the opinion) as well as a qualitative opinion (e.g.
Tags have recently become popular as a means of annotating and organizing Web pages and blog entries. Advocates of tagging argue that the use of tags produces a'folksonomy', a system in which the meaning of a tag is determined by its use among the community as a whole. We analyze the effectiveness of tags for classifying blog entries by gathering the top 350 tags from Technorati and measuring the similarity of all articles that share a tag. We find that tags are useful for grouping articles into broad categories, but less effective in indicating the particular content of an article. We then show that automatically extracting words deemed to be highly relevant can produce more focused categorization of articles. We also provide anecdotal evidence of some of tagging's weaknesses, and discuss future directions that could make tagging more effective as a tool for information organization and retrieval.
Blogspace is one of the most dynamic areas of today's Internet, and it is increasingly recognised that blogs are much more than "meaningless chatter". Many syntaxbased approaches exist to analyse the text and the network structure between blogs. While this is very helpful for purposes such as the detection of discussion bursts concerning uniquely-named topics (e.g., a book, product, or person), it is insufficient for understanding blogs discussing new phenomena in different wordings, or for finding and explaining relationships between new discourse topics or the context of a new topic in a larger domain of discourse. In this paper, we propose two methods for semantics-enhanced blogs analysis that allow the analyst to integrate domain-specific as well as general background knowledge. The methods rely on the Term Extractor for identifying keyphrases (Navigli & Velardi, 2004), SSI (Structural Semantic Interconnections) for disambiguating terms (Navigli & Velardi, 2005), and the taxonomy of domain labels by (Magnini & Cavaglià, 2000). Applications include topic detection and grouping, the proposal of blog tags and the forming of blog directories, and blog recommender systems. To illustrate the usefulness of our approach, we present a detailed experimental analysis of a sample of four sets of blogs with different thematic foci (food, health, law, and weblogs about blogging).
Brooks and Nancy Montanez An Exploration of Observable Features Related to Blogger Age / 15 John D. Burger and John C. Henderson Opinion Analysis in Document Databases / 21 Carmine Cesarano, Antonio Picariello, Diego Reforgiato, Amelia Sagoff, V. S. Subrahmanian, and Bonnie Dorr Using Verbs and Adjectives to Automatically Classify Blog Sentiment / 27 Paula Chesley, Bruce Vincent, Li Xu, and Rohini K. Srihari Highlights from 12 Months of Blogs / 30 Christine Doran, John Griffith, and John Henderson Users' Behavioral Analysis on Weblogs / 34 Tadanobu Furukawa, Yutaka Matsuo, Tomofumi Matsuzawa, Masayuki Takeda, and Koki Uchiyama Mining the Blogosphere for Contributors' Sentiments / 37 Boris Galitsky and Boris Kovalerchuk Distinguishing Affective States in Weblog Posts / 40 Michel Généreux and Roger Evans Indexing Weblogs One Post at a Time / 43 Natalie Glance How Do Blog Gardens Grow?