scholarly journals Employee Performance Evaluation Using Sentiment Analysis

2021 ◽  
Vol 11 (2) ◽  
pp. 2086-2095
Author(s):  
Dr.R. Sasikumar ◽  
Badi Alekhya ◽  
K. Harshita ◽  
O.S. Hema Sree
Keyword(s):  
Data Mining ◽  
Text Classification ◽  
Semantic Analysis ◽  
Employee Performance ◽  
Mining Activity ◽  
Analysis Program ◽  
Machine Learning Methods ◽  
Language Analysis ◽  
Polarity Change ◽  
Natural Language 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.

scholarly journals Data Mining and Machine Learning Methods Applied to A Numerical Clinching Model

2018 ◽  
Vol 117 (3) ◽  
pp. 387-423
Author(s):  
Marco Götz ◽  
Ferenc Leichsenring ◽  
Thomas Kropp ◽  
Peter Müller ◽  
Tobias Falk ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Learning Methods ◽  
Machine Learning Methods

Using Association Rules for Query Reformulation

Data Mining ◽  
2013 ◽  
pp. 503-514
Author(s):  
Ismaïl Biskri ◽  
Louis Rompré
Keyword(s):  
Data Mining ◽  
Information Retrieval ◽  
Association Rules ◽  
Text Classification ◽  
Large Volume ◽  
Query Reformulation ◽  
Long Time ◽  
Hidden Knowledge

In this paper the authors will present research on the combination of two methods of data mining: text classification and maximal association rules. Text classification has been the focus of interest of many researchers for a long time. However, the results take the form of lists of words (classes) that people often do not know what to do with. The use of maximal association rules induced a number of advantages: (1) the detection of dependencies and correlations between the relevant units of information (words) of different classes, (2) the extraction of hidden knowledge, often relevant, from a large volume of data. The authors will show how this combination can improve the process of information retrieval.

Towards Behaviour Recognition with Unlabelled Sensor Data

2013 ◽  
pp. 86-110
Author(s):  
Sook-Ling Chua ◽  
Stephen Marsland ◽  
Hans W. Guesgen
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Inverse Problem ◽  
Sensor Data ◽  
Training Set ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Using Data ◽  
Symbolic Approach ◽  
Behaviour Recognition

The problem of behaviour recognition based on data from sensors is essentially an inverse problem: given a set of sensor observations, identify the sequence of behaviours that gave rise to them. In a smart home, the behaviours are likely to be the standard human behaviours of living, and the observations will depend upon the sensors that the house is equipped with. There are two main approaches to identifying behaviours from the sensor stream. One is to use a symbolic approach, which explicitly models the recognition process. Another is to use a sub-symbolic approach to behaviour recognition, which is the focus in this chapter, using data mining and machine learning methods. While there have been many machine learning methods of identifying behaviours from the sensor stream, they have generally relied upon a labelled dataset, where a person has manually identified their behaviour at each time. This is particularly tedious to do, resulting in relatively small datasets, and is also prone to significant errors as people do not pinpoint the end of one behaviour and commencement of the next correctly. In this chapter, the authors consider methods to deal with unlabelled sensor data for behaviour recognition, and investigate their use. They then consider whether they are best used in isolation, or should be used as preprocessing to provide a training set for a supervised method.

Detection of Economy-Related Turkish Tweets Based on Machine Learning Approaches

2022 ◽  
pp. 171-195
Author(s):  
Jale Bektaş
Keyword(s):  
Machine Learning ◽  
Text Mining ◽  
Text Classification ◽  
Integration Method ◽  
Classification Problem ◽  
Feature Representation ◽  
Learning Approaches ◽  
Machine Learning Methods ◽  
Linguistic Approach ◽  
Turkish Language

Conducting NLP for Turkish is a lot harder than other Latin-based languages such as English. In this study, by using text mining techniques, a pre-processing frame is conducted in which TF-IDF values are calculated in accordance with a linguistic approach on 7,731 tweets shared by 13 famous economists in Turkey, retrieved from Twitter. Then, the classification results are compared with four common machine learning methods (SVM, Naive Bayes, LR, and integration LR with SVM). The features represented by the TF-IDF are experimented in different N-grams. The findings show the success of a text classification problem is relative with the feature representation methods, and the performance superiority of SVM is better compared to other ML methods with unigram feature representation. The best results are obtained via the integration method of SVM with LR with the Acc of 82.9%. These results show that these methodologies are satisfying for the Turkish language.

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