Employee Performance Evaluation Using Sentiment Analysis

Emotional analysis and data mining has become a hot topic in the field of data mining and natural language analysis as a solidly typed mining activity to analyze the concept of objects (i.e., emotion) expressed in the text. Emotional analysis is an important step in the recommendation process, because it allows you to separate the sense of the root context (e.g., positive or negative). In emotional analysis, the word-of-word (BOW) model is widely used in text classification, similar to how it is used in the modeling of a traditional theme. These two anti-emotional texts are considered very similar to the BOW representation. That is why, as a result of polarity change, machine learning methods often fail. We recommend combining a semantic analysis program with a separator to evaluate work results.

Natural Language Analysis and the Psychology of Verbal Behavior: The Past, Present, and Future States of the Field

Throughout history, scholars and laypeople alike have believed that our words contain subtle clues about what we are like as people, psychologically speaking. However, the ways in which language has been used to infer psychological processes has seen dramatic shifts over time and, with modern computational technologies and digital data sources, we are on the verge of a massive revolution in language analysis research. In this article, we discuss the past and current states of research at the intersection of language analysis and psychology, summarizing the central successes and shortcomings of psychological text analysis to date. We additionally outline and discuss a critical need for language analysis practitioners in the social sciences to expand their view of verbal behavior. Lastly, we discuss the trajectory of interdisciplinary research on language and the challenges of integrating analysis methods across paradigms, recommending promising future directions for the field along the way.

Searching for Well-Behaved Fragments of Halpern-Shoham Logic

Temporal reasoning constitutes one of the main topics within the field of Artificial Intelligence. Particularly interesting are interval-based methods, in which time intervals are treated as basic ontological objects, in opposite to point-based methods, where time-points are considered as basic. The former approach is more expressive and seems to be more appropriate for such applications as natural language analysis or real time processes verification. My research concerns the classical interval-based logic, namely Halpern-Shoham logic (HS). In particular, my investigation continues recently proposed search for well-behaved - i.e., expressive enough for practical applications and of low computational complexity - HS fragments obtained by imposing syntactical restrictions on the usage of propositional connectives in their languages.