Sentiment-analysis of review text for micro-video

Liu, Zhengzheng; Yang, Nan; Cao, Sanxing

doi:10.1109/compcomm.2016.7924756

Cited by 2 publications

(1 citation statement)

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“…For review text they are crawling, processing and finally analyzing sentimental value from online video. They have done automatically this process using sentiment dictionary algorithm and framework in [2]. They have presented an automatic technique to detect sentiment from natural audio visual stream which are available in you tube videos.…”

Section: Literature Reviewmentioning

confidence: 99%

Natural language processing based advanced method of unnecessary video detection

Moon

Salehin

Parvin

et al. 2021

IJECE

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<span>In this study we have described the process of identifying unnecessary video using an advanced combined method of natural language processing and machine learning. The system also includes a framework that contains analytics databases and which helps to find statistical accuracy and can detect, accept or reject unnecessary and unethical video content. In our video detection system, we extract text data from video content in two steps, first from video to MPEG-1 audio layer 3 (MP3) and then from MP3 to WAV format. We have used the text part of natural language processing to analyze and prepare the data set. We use both Naive Bayes and logistic regression classification algorithms in this detection system to determine the best accuracy for our system. In our research, our video MP4 data has converted to plain text data using the python advance library function. This brief study discusses the identification of unauthorized, unsocial, unnecessary, unfinished, and malicious videos when using oral video record data. By analyzing our data sets through this advanced model, we can decide which videos should be accepted or rejected for the further actions.</span>

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Section: Literature Reviewmentioning

confidence: 99%