Smart City Services Monitoring Framework using Fuzzy Logic Based Sentiment Analysis and Apache Spark

With the rapid development of mobile Internet, the social network has become an important platform for users to receive, release, and disseminate information. In order to get more valuable information and implement effective supervision on public opinions, it is necessary to study the public opinions, sentiment tendency, and the evolution of the hot events in social networks of a smart city. In view of social networks’ characteristics such as short text, rich topics, diverse sentiments, and timeliness, this paper conducts text modeling with words co-occurrence based on the topic model. Besides, the sentiment computing and the time factor are incorporated to construct the dynamic topic-sentiment mixture model (TSTS). Then, four hot events were randomly selected from the microblog as datasets to evaluate the TSTS model in terms of topic feature extraction, sentiment analysis, and time change. The results show that the TSTS model is better than the traditional models in topic extraction and sentiment analysis. Meanwhile, by fitting the time curve of hot events, the change rules of comments in the social network is obtained.

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Real time Sentiment Analysis of Tweets using Apache Spark and Scala

ACS Journal for Science and Engineering ◽

10.34293/acsjse.v1i2.9 ◽

2021 ◽

Vol 1 (2) ◽

pp. 9-15

Author(s):

V Mareeswari ◽

Sunita S Patil ◽

Ramanan G

Keyword(s):

Sentiment Analysis ◽

Real Time ◽

Ad Hoc ◽

Apache Spark ◽

Data Streaming ◽

Real Time Processing ◽

Open Source Data ◽

Textual Data ◽

Bayes Algorithm ◽

Processing Platform

Sentiment Analysis is becoming the field of focus with time considering the user experience weighs much more for the business to grow and for the studies as well. The sentimental expressions refers to the emotions or feeling of a person across certain point of focus or issues. So, in this project, with the assistance of Apache Spark Framework, an open source data streaming and processing platform, sentiment evaluation is done on the tweets from Twitter by the means of real time processing as well as an Ad-hoc Run. Some preprocessing of the textual data has been done upon for better characteristics extraction thus resulting in greater accuracy. The validation of this has been done for achieving better result by comparing the other processes when Naive Bayes algorithm is used.

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Applying Fuzzy Logic and Neural Network in Sentiment Analysis for Fake News Detection: Case of Covid-19

Studies in Computational Intelligence - Combating Fake News with Computational Intelligence Techniques ◽

10.1007/978-3-030-90087-8_19 ◽

2021 ◽

pp. 387-400

Author(s):

Bahra Mohamed ◽

Hmami Haytam ◽

Fennan Abdelhadi

Keyword(s):

Neural Network ◽

Fuzzy Logic ◽

Sentiment Analysis ◽

Fake News

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A Resource Oriented Route Selection Framework Using Contextual Information Based on Fuzzy Logic

Electronics ◽

10.3390/electronics8091023 ◽

2019 ◽

Vol 8 (9) ◽

pp. 1023 ◽

Cited By ~ 2

Author(s):

Khadak Singh Bhandari ◽

Gi Hwan Cho

Keyword(s):

Fuzzy Logic ◽

Objective Function ◽

Smart City ◽

Contextual Information ◽

Network Dynamics ◽

Residual Energy ◽

Smart Devices ◽

Route Selection ◽

Network Simulator ◽

Lossy Networks

Internet of Things (IoT) is expected to have a significant impact on city’s service provisioning and make a smart city more accessible and pragmatic since the deployment of heterogeneous smart devices in each infrastructure of cities is increasing. So far, the IPv6 routing protocol for low power and lossy networks (RPL) is considered to fit on IoT infrastructure for achieving the expected network requirements. While RPL meets the IoT network requirements quite well, there are some issues that need to be addressed, such as adaptability to network dynamics. This issue significantly limits the use of RPL in many smart city application scenarios, such as emergency alerts with high traffic flows. As part of a smart city vision, IoT applications are becoming more diverse, which requires context-awareness in routing protocols to support the behavior of the network. To address this issue, we design an objective function that performs the route selection based on fuzzy logic techniques while using contextual information from the application. For this, we present a new context-oriented objective function (COOF) that comprises both nodes as well as link metrics. Further, we suggest two new routing metrics, known as queue fluctuation index (QFI) and residual energy index (REI), which consider the status of queue utilization and remaining energy, respectively. The metrics used are designed to respond to the dynamic needs of the network. The proposed approach has been examined and evaluated in different scenarios when compared to other similar approach and default RPL objective functions. Simulation experiments are conducted in Cooja network simulator for Contiki OS. The evaluation results show that COOF can cope with network dynamics and IoT-based smart city application requirements.

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