Towards a big data framework for analyzing social media content

Jimenez-Marquez, Jose Luis; González‐Carrasco, Israel; López-Cuadrado, José Luis; Ruíz‐Mezcua, Belén

doi:10.1016/j.ijinfomgt.2018.09.003

Cited by 111 publications

(77 citation statements)

References 198 publications

(244 reference statements)

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“…In this data-driven age, DMOs can go forward and overcome competitors by integrating analysis methodologies into their business strategies [73]. However, the viability of the methodology used to analyse big data is essential for the DMO.…”

Section: Discussionmentioning

confidence: 99%

How DMO Can Measure the Experiences of a Large Territory

Franzoni¹,

Bonera²

2019

Sustainability

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This paper aims at providing a methodology for analyzing and measuring the experiences offered by a large territory by investigating online conversation on the “things to do” or in other words the experiences by TripAdvisor platform. Opinions and comments are able to influence the choice of the tourist destination and to raise specific expectations, in order to find corrective measures to be taken to preserve or enhance the interest of a tourist destination. By applying the methodology advanced, Destination Management Organization (DMO) can collect useful information in order to make decisions and take action to protect and/or increase the competitiveness of the destination. The empirical observation, thought the methodology described herein, was applied in the Province of Brescia, Italy, a large territory marked by the presence of different destinations and experiences and Bresciatourism—Visit Brescia, the DMO of this territory, was involved.

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Section: Discussionmentioning

confidence: 99%

How DMO Can Measure the Experiences of a Large Territory

Franzoni¹,

Bonera²

2019

Sustainability

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“…(2018) in order to reduce the complexity to analyze social media corpora they developed two-stage framework. In the first "machine learning model" phase they setup the TFIDF Victimizer, where the data words were stemmed to their root form by using Python Snowball Stemmer from NLTK library to decrease the corpus size and to find important words in the text [1]. [4].…”

Section: Related Workmentioning

confidence: 99%

“…Sentiment Analysis analyze and quantify users textual views and opinions posted on the social media networks. The social media datasets analytical results enable the organizations, companies and service centers to take vital decision accordingly [1] [30].Prior to apply the sentiment analysis algorithm, the social media corpora is under gone for text normalization process, where the tokens, which does not have any analytical value those tokens will be removed. Stemming is one of important phase of text normalization, where tweet words suffixes will be removed to identify the root word [14][20] [28] [29].…”

Section: Introductionmentioning

confidence: 99%

“…Natural Language Processing(NLP) is subfield of Artificial Intelligence, which understand human languages and process through machine learning methods. Natural Languages Process can understand the sentiments of the users hidden under social media textual tweets and also classify them as positive and negative [1] To achieve balanced stemming the Unprejudiced stemming algorithm is proposed.…”

Section: Introductionmentioning

confidence: 99%

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The Unprejudiced Stemmer to Prevent Etymological Behavior of Stemmed Morphemes Of Social Media Corpora

Rao*,

2019

IJITEE

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Sentiment Analysis is an application of Natural Langue Processing to analyze social media corpora to extract insights of corpora. Sentiment analytical results are the real feedback of the customers, which enables the organizations and companies to take appropriate decision on their products and business policies. Stemming plays in-evitable and vital role in sentiment analysis. Stemming is one of the phase of preprocessing the social media corpora. Today most of the researches uses strong stemmers to identify stem words of social media corpora. The most popular stemming algorithms such as Lancaster and Porter stemming algorithms causes prejudiced the meaning of the words. The over-stemmed words mislead the sentiment classification process. To prevent the over-stemming the Unprejudiced lighter stemming algorithm is proposed to sustain the meaning of the stemmed words. The propose Un-prejudiced algorithm uses lexical database and Parts of speech of Python Natural Language Tool Kit. There are a few stemming algorithm accuracy evaluation methods, in this paper we focused on Paice Error-rate relative to truncation (ERRT) measure to evaluate the accuracy of Lancaster, Porter and Unprejudiced stemming algorithms. The experiments were conducted on 25,758 source words and results were evaluated using Paice stem evaluation method and Sirsat method. The Paice Evaluation ERRT values 0.47209, 0.28703, 0.15502 of Lancaster, Porter, Unprejudiced respectively are proved that the Unprejudiced stemmer is more accurate than Lancaster and Porter. Sirsat’s stem evaluation method Average Words Conflation Factor (AWCF) results 10310.31, 14031.17, 23349.87 of Lancaster, Porter, Unprejudiced respectively are also proved the Unprejudiced stemming algorithm is more accurate than Lancaster and Porter stemming algorithms.

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“…Since smart devices with good performance were penetrated, since high-speed wired and wireless network technologies were developed, and since small-sized display devices with good image quality were developed, people have easily acquired various types of media content, such as pictures, animation, music files, user created contents (UCC), and videos, at any time and any place through the Internet [1][2][3][4][5]. Such acquired media content is usefully applied to various application fields, including big data analysis, image security, deep learning-based artificial intelligence, sustainable anticipatory computing, media content indexing and retrieval, and Internet of Things (IoT) [6][7][8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Robust Blocking of Human Faces with Personal Information Using Artificial Deep Neural Computing

Jang

Lee

2020

Sustainability

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High-speed wired and wireless Internet are one of the useful ways to acquire various types of media data easily. In this circumstance, people also can easily get media data including objects with exposed personal information through the Internet. Exposure of personal information emerges as a social issue. This paper proposes an effective blocking technique that makes it possible to robustly detect target objects with exposed personal information from various types of input images with the use of deep neural computing and to effectively block the detected objects' regions. The proposed technique first utilizes the neural computing-based learning algorithm to robustly detect the target object including personal information from an image. It next generates a grid-type mosaic and lets the mosaic overlap the target object region detected in the previous step so as to effectively block the object region that includes personal information. Experimental results reveal that the proposed algorithm robustly detects the target object region with exposed personal information from a variety of input images and effectively blocks the detected region through grid-type mosaic processing. The object blocking technique proposed in this paper is expected to be applied to various application fields such as image security, sustainable anticipatory computing, object tracking, and target blocking.

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Towards a big data framework for analyzing social media content

Abstract: El artículo está incluido parcialmente en la tesis. ▪ Capítulos en los que se incluye el material de dicha contribución: 2 y 3. ▪ Todo material de esta fuente incluido en la tesis está señalado por medios tipográficos y una referencia explícita.

Cited by 111 publications

References 198 publications

How DMO Can Measure the Experiences of a Large Territory

How DMO Can Measure the Experiences of a Large Territory

The Unprejudiced Stemmer to Prevent Etymological Behavior of Stemmed Morphemes Of Social Media Corpora

Robust Blocking of Human Faces with Personal Information Using Artificial Deep Neural Computing

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