A multi-label, semi-supervised classification approach applied to personality prediction in social media

Sentiment analysis is one of the fastest growing fields with its demand and potential benefits that are increasing every day. Sentiment analysis aims to classify the polarity of a document through natural language processing, text analysis. With the help of internet and modern technology, there has bee n a tremendous growth in the amount of data. Each individual is in position to precise his/her own ideas freely on social media. All of this data can be analyzed and used in order to draw benefits and quality information. In this paper, the focus is on cyber-hate classification based on for public opinion or views, since the spread of hate speech using social media can have disruptive impacts on social sentiment analysis. In particular, here proposing a modified approach with two stage training for dealing with text ambiguity and classifying three type approach positive, negative and neutral sentiment, and compare its performance with those popular methods also as well as some existing fuzzy approaches. Afterword comparing the performance of proposed approach with commonly used sentiment classifiers which are known to perform well in this task. The experimental results indicate that our modified approach performs marginally better than the other algorithms.

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Improved Use of Drone Imagery for Malaria Vector Control through Technology-Assisted Digitizing (TAD)

Remote Sensing ◽

10.3390/rs14020317 ◽

2022 ◽

Vol 14 (2) ◽

pp. 317

Author(s):

Andy Hardy ◽

Gregory Oakes ◽

Juma Hassan ◽

Yussuf Yussuf

Keyword(s):

Vector Control ◽

Malaria Vector ◽

Supervised Classification ◽

Region Growing ◽

Malaria Vector Control ◽

Larval Habitats ◽

Classification Approach ◽

Spatial Intelligence ◽

Supervised Image Classification ◽

Complex Features

Drones have the potential to revolutionize malaria vector control initiatives through rapid and accurate mapping of potential malarial mosquito larval habitats to help direct field Larval Source Management (LSM) efforts. However, there are no clear recommendations on how these habitats can be extracted from drone imagery in an operational context. This paper compares the results of two mapping approaches: supervised image classification using machine learning and Technology-Assisted Digitising (TAD) mapping that employs a new region growing tool suitable for non-experts. These approaches were applied concurrently to drone imagery acquired at seven sites in Zanzibar, United Republic of Tanzania. Whilst the two approaches were similar in processing time, the TAD approach significantly outperformed the supervised classification approach at all sites (t = 5.1, p < 0.01). Overall accuracy scores (mean overall accuracy 62%) suggest that a supervised classification approach is unsuitable for mapping potential malarial mosquito larval habitats in Zanzibar, whereas the TAD approach offers a simple and accurate (mean overall accuracy 96%) means of mapping these complex features. We recommend that this approach be used alongside targeted ground-based surveying (i.e., in areas inappropriate for drone surveying) for generating precise and accurate spatial intelligence to support operational LSM programmes.

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Service complaint identification in hotel social media: A two-step classification approach

International Journal of Electrical Engineering Education ◽

10.1177/0020720920928467 ◽

2020 ◽

pp. 002072092092846

Author(s):

Jiahua Jin ◽

Lu Lu

Keyword(s):

Social Media ◽

Nearest Neighbor ◽

Support Vector ◽

Classification Algorithms ◽

Construction Process ◽

K Nearest Neighbor ◽

Classification Approach ◽

Consumer Complaint ◽

Training Samples ◽

Binary Classifiers

Hotel social media provides access to dissatisfied customers and their experiences with services. However, due to massive topics and posts in social media, and the sparse distribution of complaint-related posts and, manually identifying complaints is inefficient and time-consuming. In this study, we propose a supervised learning method including training samples enlargement and classifier construction. We first identified reliable complaint and noncomplaint samples from the unlabeled dataset by using small labeled samples as training samples. Combining the labeled samples and enlarged samples, classification algorithms support vector machine and k-nearest neighbor were then adopted to build binary classifiers during the classifier construction process. Experimental results indicate the proposed method can identify complaints from social media efficiently, especially when the amount of labeled training samples is small. This study provides an efficient approach for hotel companies to distinguish a certain kind of consumer complaint information from large number of unrelated information in hotel social media.

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