Τεχνικές αναλύσης κοινωνικών δικτύων, με έμφαση σε γράφους εμπιστοσύνης

Mapping Intimacies ◽

10.12681/eadd/39774 ◽

2015 ◽

Author(s):

Αθανάσιος Παπαοικονόμου

Keyword(s):

Deep Learning ◽

Natural Language Processing ◽

Social Network ◽

Social Network Analysis ◽

Network Analysis ◽

Natural Language ◽

Language Processing

Η παρούσα διατριβή προτείνει τεχνικές για την ανάλυση κοινωνικών δικτύων δίνοντας ιδιαίτερη έμφαση σε δίκτυα στα οποία οι χρήστες μπορούν να εκφράζουν εμπιστοσύνη ή δυσπιστία μεταξύ τους. Η ανάλυση τέτοιων γράφων εμπιστοσύνης είναι ένα ενδιαφέρον πρόβλημα με ευρύ φάσμα εφαρμογών όπως η ανάλυση γεωπολιτικών σχέσεων και η εύρεση κοινοτήτων χρηστών. Στα πρώτα τρία κεφάλαια εξετάζεται το πρόβλημα της πρόβλεψης της προδιάθεσης ενός χρήστη για έναν άλλο, αντλώντας τεχνικές από τρεις διαφορετικούς τομείς. Αρχικά, χρησιμοποιούνται κλασικές και διαδεδομένες τεχνικές από τον χώρο της Ανάλυσης Κοινωνικών Δικτύων (Social Network Analysis) με σκοπό να ερευνηθούν οι μηχανισμοί διάδοσης θετικών και αρνητικών απόψεων στο δίκτυο. Έπειτα, ενσωματώνουμε τεχνικές από τον τομέα της Βιοστατιστικής, ώστε να αναλύσουμε μεγάλα κοινωνικά δίκτυα από μικροσκοπική σκοπιά. Στη συνέχεια, με χρήση τεχνικών deep learning δείχνουμε πως είναι δυνατόν να "κατασκευαστεί" ένας γράφος εμπιστοσύνης αξιοποιώντας δεδομένα φαινομενικά άσχετα με αυτόν τον σκοπό, όπως οι κριτικές των χρηστών για διάφορα προϊόντα. Στο τελευταίο κεφάλαιο, παρουσιάζουμε έναν αλγόριθμο εύρεσης κοινοτήτων σε κοινωνικά δίκτυα, βασιζόμενοι σε πρόσφατες προόδους στον τομέα της Ανάλυσης Φυσικής Γλώσσας (Natural Language Processing). Η σειρά των κεφαλαίων αποτυπώνει την χρονική σειρά των πειραμάτων που εφάρμοσα αλλά κάθε κεφάλαιο είναι γραμμένο ώστε να μην έχει σημαντικές συσχετίσεις με τα προηγούμενα και έτσι να μπορεί να διαβαστεί αυτόνομα

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Multimodal Social Network Dataset Based on Goldbach Conjecture Proved Event in Zhihu

10.20944/preprints202111.0511.v1 ◽

2021 ◽

Author(s):

Tingzhen Liu ◽

Shijie Geng ◽

Zhiquan Huang ◽

Senxin Wu ◽

Zixi Wang

Keyword(s):

Natural Language Processing ◽

Social Network ◽

Social Network Analysis ◽

Network Analysis ◽

Natural Language ◽

Language Processing ◽

Dynamic Features ◽

Fine Grained ◽

Before And After ◽

The Impact

At the end of 2018, a high school student asked a question in Zhihu community, claiming that he had proved Goldbach's conjecture. The problem caused an explosive reaction and a large number of users participated in the discussion. And has caused the widespread influence. On January 1, 2019, the questioner issued his "proof". His proof was soon proved wrong. The heated discussion caused by the incident contains a lot of information of social science analysis value. Therefore, we follow up the event in the first time and build a time series dataset for the event. Taking the questioner's "proof" as the dividing line, all the answers, comments, sub comments and user information of writing these texts before and after two days were recorded. This series of temporal information can reflect the dynamic features of the interaction between user opinions, and the impact of exogenous shocks (proof release) on community opinions. The dataset can be used not only for the demonstration of various social network analysis algorithms, but also for a series of natural language processing tasks such as fine-grained sentiment analysis for long texts, as well as multimodal tasks combining natural language processing and social network analysis. This paper introduces the characteristics and structure of the dataset, shows the visualization effect of social network, and uses the dataset train the benchmark model of emotion analysis.

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Keyword extraction for film reviews based on social network analysis and natural language technology

E3S Web of Conferences ◽

10.1051/e3sconf/202018903019 ◽

2020 ◽

Vol 189 ◽

pp. 03019

Author(s):

Quan Yanan ◽

Tan Fuqiang

Keyword(s):

Social Network ◽

Social Network Analysis ◽

Network Analysis ◽

Natural Language ◽

Language Processing ◽

Semantic Analysis ◽

Semantic Network ◽

Keyword Extraction ◽

Main Stream ◽

Language Technology

At present, there are many movie reviews appear on main stream websites, and these evaluations are quite different to the same movie. As a customer, how to choose your favorite movie and television program? To solve this problem, this study attempts to use the semantic analysis of word vectors (Word2vec) semantic analysis in machine learning as a research tool to mine a large number of movie reviews. The research shows that most movie reviews have a certain theme cohesion and their semantic network has quite connected. Through the use of social network analysis and the use of Word2vec word vector technology in natural language processing, it is possible to present a streamlined movie review based on movie review network semantics and keyword extraction, thus helping to select the favorite movie review.

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Intelligent Estimation of Social Media Sentimental Features using Deep Learning with Natural Language Processing Strategies

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.f8825.0410621 ◽

2021 ◽

Vol 10 (6) ◽

pp. 74-79

Author(s):

Sarojini Yarramsetti ◽

Anvar Shathik J ◽

Renisha. P.S.

Keyword(s):

Social Media ◽

Deep Learning ◽

Natural Language Processing ◽

Social Network ◽

Natural Language ◽

Language Processing ◽

Digital World ◽

The Social ◽

Learning Principles ◽

The Voice

In this digital world, experience sharing, knowledge exploration, taught posting and other related social exploitations are common to every individual as well as social media/network such as FaceBook, Twitter, etc plays a vital role in such kinds of activities. In general, many social network based sentimental feature extraction details and logics are available as well as many researchers work on that domain for last few years. But all those research specification are narrowed in the sense of building a way for estimating the opinions and sentiments with respect to the tweets and posts the user raised on the social network or any other related web interfacing medium. Many social network schemes provides an ability to the users to push the voice tweets and voice messages, so that the voice messages may contain some harmful as well as normal and important contents. In this paper, a new methodology is designed called Intensive Deep Learning based Voice Estimation Principle (IDLVEP), in which it is used to identify the voice message content and extract the features based on the Natural Language Processing (NLP) logic. The association of such Deep Learning and Natural Language Processing provides an efficient approach to build the powerful data processing model to identify the sentimental features from the social networking medium. This hybrid logic provides support for both text based and voice based tweet sentimental feature estimations. The Natural Language Processing principles assists the proposed approach of IDLVEP to extracts the voice content from the input message and provides a raw text content, based on that the deep learning principles classify the messages with respect to the estimation of harmful or normal tweets. The tweets raised by the user are initially sub-divided into two categories such as voice tweets and text tweets. The voice tweets will be taken care by the NLP principles and the text enabled tweets will be handled by means of deep learning principles, in which the voice tweets are also extracted and taken care by the deep learning principle only. The social network has two different faces such as provides support to developments as well as the same it provides a way to access that for harmful things. So, that this approach of IDLVEP identifies the harmful contents from the user tweets and remove that in an intelligent manner by using the proposed approach classification strategies. This paper concentrates on identifying the sentimental features from the user tweets and provides the harm free social network environment to the society.

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