Pathfinder network Several psychometric scaling methods start from proximity data and yield structures revealing the underlying organization of the data. Data clustering and multidimensional scaling are two such methods. Network scaling represents another method based on graph theory. Pathfinder networks are derived from proximities for pairs of entities. Proximities can be obtained from similarities, correlations, distances, conditional probabilities, or any other measure of the relationships among entities. The entities are often concepts of some sort, but they can be anything with a pattern of relationships. Here is an example of an undirected Pathfinder network derived from average similarity ratings of a group of biology graduate students. The Pathfinder algorithm uses two parameters. (1) The q parameter constrains the number of indirect proximities examined in generating the network. References[edit] Schvaneveldt, R. A shorter article summarizing Pathfinder networks: Schvaneveldt, R.
Competitive Intelligence functionality: Sentiment Analysis | Competitive Intelligence You have launched a new product onto the market, and of course you want to know how your customers feel about it. Are they happy about it? Disappointed? Or maybe you want to know how your customers (and other people) feel about your company in general? It is difficult to find reliable answers to these sorts of questions. In the previous two posts, ‘What is a proper Competitive Intelligence tool?’ Structured data is data that is available as a certain field in a database. The latter certainly applies to sentiment analysis as well. If you’d search for the word “Google”, the following results (among others) could come up: “Google search works very nice!” In tools performing sentiment analysis, it is very likely that the sentences are being parsed. While above sentences are very simplistic and relatively easy to classify, this is much harder for ironic or sarcastic sentences for instance. Despite the difficulties in obtaining and analyzing unstructured data, it can be amazingly valuable.
Latent semantic analysis Latent semantic analysis (LSA) is a technique in natural language processing, in particular in vectorial semantics, of analyzing relationships between a set of documents and the terms they contain by producing a set of concepts related to the documents and terms. LSA assumes that words that are close in meaning will occur in similar pieces of text. A matrix containing word counts per paragraph (rows represent unique words and columns represent each paragraph) is constructed from a large piece of text and a mathematical technique called singular value decomposition (SVD) is used to reduce the number of columns while preserving the similarity structure among rows. Words are then compared by taking the cosine of the angle between the two vectors formed by any two rows. Values close to 1 represent very similar words while values close to 0 represent very dissimilar words.[1] Overview[edit] Occurrence matrix[edit] Rank lowering[edit] Derivation[edit] Let be a matrix where element in document ). and
Factor analysis Factor analysis is related to principal component analysis (PCA), but the two are not identical. Latent variable models, including factor analysis, use regression modelling techniques to test hypotheses producing error terms, while PCA is a descriptive statistical technique.[1] There has been significant controversy in the field over the equivalence or otherwise of the two techniques (see exploratory factor analysis versus principal components analysis).[citation needed] Statistical model[edit] Definition[edit] Suppose we have a set of observable random variables, with means Suppose for some unknown constants and unobserved random variables , where , we have Here, the are independently distributed error terms with zero mean and finite variance, which may not be the same for all . , so that we have In matrix terms, we have If we have observations, then we will have the dimensions , and . denote values for one particular observation, and matrix does not vary across observations. and are independent. . or .
Google Search - Google Gamed by Cyber-Bullies, Needs Sentiment Analysis The New York Times has a great exposé on how Vitaly Borker and his online DecorMyEyes eyeglass business uses negativity to bolster his PageRank and profits. Search Engine Land's Danny Sullivan is consulted in the piece and blogged about it, summing it up thusly: Any publicity, even negative publicity, means a win with Google's ranking algorithms. Is he right? Maybe. Certainly the story illustrates the fallacy of Google's "gold standard" search results. First, Borker is beyond rude and mean. Second, as a media man, I see parallels between what Borker is doing and what Hollywood handlers do to thrust faded personalities back into the limelight under the "all press is good press" technique. I respect how that works. I'll let you read the story for yourself, but I'm going to assume up front that Sullivan is right that Google does not use sentiment analysis as a search signal. Yes, but people would still be able to find the results they need. Maybe that would work.
twitrratr Co-citation Figure visualizing co-citation on the left and a refinement of co-citation, Co-citation Proximity Analysis (CPA) on the right. Co-citation, like Bibliographic Coupling, is a semantic similarity measure for documents that makes use of citation relationships. Co-citation is defined as the frequency with which two documents are cited together by other documents.[1] If at least one other document cites two documents in common these documents are said to be co-cited. The more co-citations two documents receive, the higher their co-citation strength, and the more likely they are semantically related.[1] The figure to the right illustrates the concept of co-citation and a more recent variation of co-citation which accounts for the placement of citations in the full text of documents. The figure's right image shows a citing document which cites the Documents 1, 2 and 3. Over the decades, researchers proposed variants or enhancements to the original co-citation concept. Considerations[edit]
10 Web Tools To Try Out Sentiment Search & Feel the Pulse Sentiment analysis or opinion mining has long been a part of data analysis. Surveys and polls are the old world tools for measuring the pulse of the crowd. Web 2.0 brought in the flood. Those little thumbs up or thumbs down icons you see next to a web entry are sentiment capturing tools. Searching for sentiment on the web is measured against what’s positive, what’s negative, and what remains neutral. Because sentiment search can flesh out a general idea of what’s good or bad about something. So, let’s try out some sentiment search tools and see if we can catch the moods. Rankspeed We kick off our list with Rankspeed. Results are expressed as percentages matching the sentiments. SocialMention SocialMention searches social media outlets like blogs, Digg, Twitter, FriendFeed, Facebook, YouTube, Google and a lot more. The results can be broken down by sentiments, strength, passion, and reach. Power users can set up email alerts and also download the results in a CSV/Excel file. CrowdEye Jodange
family/developer GATE Developer is a development environment that provides a rich set of graphical interactive tools for the creation, measurement and maintenance of software components for processing human language. GATE Developer is open source software, available under the GNU Lesser General Public Licence 3.0, and can be downloaded from this page. (GATE Developer and GATE Embedded are bundled, and in older distributions were refered to just as "GATE".) Uses Language processing software uses specialised data structures and algorithms such as annotation graphs, finite state machines or support vector machines. GATE Developer aids the creation of these complex structures, the visualisation of processing results, and the measurement of their accuracy relative to manually or semi-automatically produced results. Developer is a specialist tool similar in purpose and character to a programmer's integrated development environment (which is one reason we call it "the Eclipse of natural language processing").
Domain analysis In software engineering, domain analysis, or product line analysis, is the process of analyzing related software systems in a domain to find their common and variable parts. It is a model of wider business context for the system. The term was coined in the early 1980s by James Neighbors.[1][2] Domain analysis is the first phase of domain engineering. It is a key method for realizing systematic software reuse.[3] Domain analysis produces domain models using methodologies such as domain specific languages, feature tables, facet tables, facet templates, and generic architectures, which describe all of the systems in a domain. Several methodologies for domain analysis have been proposed.[4] The products, or "artifacts", of a domain analysis are sometimes object-oriented models (e.g. represented with the Unified Modeling Language (UML)) or data models represented with entity-relationship diagrams (ERD). Domain analysis techniques[edit] References[edit] Jump up ^ Neighbors, J.M. See also[edit]
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