Opinion Mining And Sentiment Analysis

He divided English idiomatic expression into three types such as: phrasal verb, prepositional verb, and partial verb. Then the meaning of the idioms were analyzed semantically based on theory of Semantic Triangle who proposed by Ogden and Richards in Palmer (1976) and they stated that, we need to divide an idiom into its main aspects such as symbol, thought or reference, and referent to get the intended

This theory presents rules of grammar in terms of functions of words in sentences such as Subject, Predicate, Object, and Adverbial. In doing so, language becomes a tool for constructing meaning to represent knowledge. Hence, human beings can interpret and represent the world for each other and for themselves (Matthiessen and Halliday, 1997. pp. 1-3). The following table (2-1) shows the three lines of meaning in the clause according to Halliday and Matthiessen

Recruiters and HR partners are constantly turning to social media to market their companies to prospective applicants (Laick & Dean, 2011). This trend is called “social hiring” and it is largely driven by the millennials who are constantly changing jobs. According to (Schramm, 2016) 54% of recruiters use Facebook, 8% use Google+ and Youtube; and 4% use Pinterest. Social media usage does come with its own risks, however companies have recognised that social media does come with a lot of benefits including effective marketing, internal and external

This pattern, represents an episode. It is similarly learnt as weight adjustments on the connections between F2 layer and selected category of F3 layer. An episode can be recognized based on the selected node in F3 layer and can be reproduced by a readout process. The corresponding events can be read out from F2 to F1 layer. Thus encoding, storing and retrieval of events is performed based on computational principles and

Daniel Riff introduced in his book the major definition and compiled their aspects into his own. He says: “Quantitative content analysis is the systematic and replicable examination of symbols of communication, which have been assigned numeric values according to valid measurement rules, and the analysis of relationships involving those values using statistical methods, to describe the communication, draw inferences about its meaning, or infer from the communication

Introduction of LINQ (Language Integrated Query) Based on the research conducted, the term LINQ is an acronyms that stand for Language Integrated Query. LINQ is tool that mainly used for querying data in the ASP.NET by defining a common programming syntax to query different type of data through using a common language. LINQ syntax is divided into 2 type of syntax which is Lambda (Method) Syntax and Query (Comprehension) Syntax. However, both syntax also involve the use of extension method. Moreover, LINQ can be used to extract data from various data sources such as LINQ to Objects, XML, SQL and etc.

Now a days when everybody is so addicted to social media and everyone is an opinionated person, people frequently and openly share their views on any happenings, events, political situation, product, entertainment industry etc. It would be quite likable and perplexing to work on this particular aspect. Sentiment analysis can be done at several level of granularity. Figure 2 shows some of the application areas of sentiment analysis. Sentiment analysis and many other text processing based research areas highly require deep natural language processing algorithms to solve various problems in the respective fields.

What is Discourse Analysis? Discourse analysis is basically a common term for a range of approaches to analyze written, vocal, or sign language use or any significant semiotic event. Discourse analysis is usually viewed as language sentence or the clause. It is the look of linguistics that's concerned about how we build up meaning in larger communicative, instead of grammatical units. It studies meaning in text, paragraph and conversation, rather than in single sentence.

The proposed approach accounts for both types of uncertainties (i.e., probabilistic and possibilistic) as well as the interdependencies that exist among different events. The flowchart diagram of different stages of quantitative FTA using the proposed hybrid uncertainty analysis method is depicted in Fig. 1. As shown in this figure, in the first step the objectives of analysis using FT are identified. The top event is identified and the scope of FTA is defined in the second and third steps.

The overall recognition performance is calculated based on word substitution, deletion and insertion errors found during recognition. Number of error counts will be displayed upon recognition [3&4]. Below Sections describes the detailed methodology of a work includes, Feature extraction technique, i.e. MFCC, Pattern Recognition Technique i.e. Building Hidden Markov Models, Decoding method using Viterbi decoder, complete HTK Process, obtained results from the work, conclusion and references used for the