Analysis of product Twitter data though opinion mining

Fernandes, Roshan; D’Souza, Rio

doi:10.1109/indicon.2016.7839025

Cited by 15 publications

(9 citation statements)

References 5 publications

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“…During the data collection stage, the results came in separate files in JSON (JavaScript Object Notation) format by using a python-based [86] Weibo Application Programming Interface (API) [87]. JSON is considered one of the most widely used standard data formats and most major programming languages already have public reader and writer modules for it [88,89]. In order to be analyzed properly and stored in the database with the selected software, the dataset was transformed into one single file in CSV (Comma-Separated Values) format so all the check-ins could be listed regarding their publishing time.…”

Section: Dataset and Study Areamentioning

confidence: 99%

Big Data Analysis to Observe Check-in Behavior Using Location-Based Social Media Data

Muhammad

Wan

2018

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With rapid advancement in location-based services (LBS), their acquisition has become a powerful tool to link people with similar interests across long distances, as well as connecting family and friends. To observe human behavior towards using social media, it is essential to understand and measure the check-in behavior towards a location-based social network (LBSN). This check-in phenomenon of sharing location, activities, and time by users has encouraged this research on the frequency of using an LBSN. In this paper, we investigate the check-in behavior of several million individuals, for whom we observe the gender and their frequency of using Chinese microblog Sina Weibo (referred as “Weibo”) over a period in Shanghai, China. To produce a smooth density surface of check-ins, we analyze the overall spatial patterns by using the kernel density estimation (KDE) by using ArcGIS. Furthermore, our results reveal that female users are more inclined towards using social media, and a difference in check-in behavior during weekday and weekend is also observed. From the results, LBSN data seems to be a complement to traditional methods (i.e., survey, census) and is used to study gender-based check-in behavior.

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Section: Dataset and Study Areamentioning

confidence: 99%

Big Data Analysis to Observe Check-in Behavior Using Location-Based Social Media Data

Muhammad

Wan

2018

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“…The download of Weibo data was the main task in the data collection and storage stage, which was achieved by using a python-based Weibo application programming interface (API) an open interface of Sina Weibo [78], which resulted in separate files in JavaScript Object Notation (JSON) format. However, JSON is used as a standard data format and is considered a dominant programming language with the availability of public modules (reading and writing) [79,80]. All the check-ins downloaded in multiple JSON files were then transformed into a single comma-separated values (CSV) format file to list the check-ins according to space and time.…”

Section: Methodsmentioning

confidence: 99%

Visualization, Spatiotemporal Patterns, and Directional Analysis of Urban Activities Using Geolocation Data Extracted from LBSN

Muhammad

Wan

Gwiazdzinski

2020

IJGI

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Location-based social networks (LBSNs) have rapidly prevailed in China with the increase in smart devices use, which has provided a wide range of opportunities to analyze urban behavior in terms of the use of LBSNs. In a LBSN, users socialize by sharing their location (also referred to as “geolocation”) in the form of a tweet (also referred to as a “check-in”), which contains information in the form of, but is not limited to, text, audio, video, etc., which records the visited place, movement patterns, and activities performed (e.g., eating, living, working, or leisure). Understanding the user’s activities and behavior in space and time using LBSN datasets can be achieved by archiving the daily activities, movement patterns, and social media behavior patterns, thus representing the user’s daily routine. The current research observing and analyzing urban activities behavior was often supported by the volunteered sharing of geolocation and the activity performed in space and time. The objective of this research was to observe the spatiotemporal and directional trends and the distribution differences of urban activities at the city and district levels using LBSN data. The density was estimated, and the spatiotemporal trend of activities was observed, using kernel density estimation (KDE); for spatial regression analysis, geographically weighted regression (GWR) analysis was used to observe the relationship between different activities in the study area. Finally, for the directional analysis, to observe the principle orientation and direction, and the spatiotemporal movement and extension trends, a standard deviational ellipse (SDE) analysis was used. The results of the study show that women were more inclined to use social media compared with men. However, the activities of male users were different during weekdays and weekends compared to those of female users. The results of the directional analysis at the district level reflect the change in the trajectory and spatiotemporal dynamics of activities. The directional analysis at the district level reveals its fine spatial structure in comparison to the whole city level. Therefore, LBSN can be considered as a supplementary and reliable source of social media big data for observing urban activities and behavior within a city in space and time.

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“…In turn, the crawler collects the check-in data filtered by gender, and the results are processed with entries that have geolocation. The outcome is in single JSON (JavaScript Object Notation) file by utilizing a python-based Weibo API (an open interface of Sina Weibo) [132,133], which is considered an extensively used data format [134,135]. To be adequately analyzed, the dataset is converted into a distinct file in the CSV (Comma-Separated Values) format so that the check-ins could be listed regarding their publishing time.…”

Section: Methodsmentioning

confidence: 99%

Spatiotemporal Analysis to Observe Gender Based Check-In Behavior by Using Social Media Big Data: A Case Study of Guangzhou, China

2019

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In a location-based social network, users socialize with each other by sharing their current location in the form of “check-in,” which allows users to reveal the current places they visit as part of their social interaction. Understanding this human check-in phenomenon in space and time on location based social network (LBSN) datasets, which is also called “check-in behavior,” can archive the day-to-day activity patterns, usage behaviors toward social media, and presents spatiotemporal evidence of users’ daily routines. It also provides a wide range of opportunities to observe (i.e., mobility, urban activities, defining city boundary, and community problems in a city). In representing human check-in behavior, these LBSN datasets do not reflect the real-world events due to certain statistical biases (i.e., gender prejudice, a low frequency in sampling, and location type prejudice). However, LBSN data is primarily considered a supplement to traditional data sources (i.e., survey, census) and can be used to observe human check-in behavior within a city. Different interpretations are used elusively for the term “check-in behavior,” which makes it difficult to identify studies on human check-in behavior based on LBSN using the Weibo dataset. The primary objective of this research is to explore human check-in behavior by male and female users in Guangzhou, China toward using Chinese microblog Sina Weibo (referred to as “Weibo”), which is missing in the existing literature. Kernel density estimation (KDE) is utilized to explore the spatiotemporal distribution geographically and weighted regression (GWR) method was applied to observe the relationship between check-in and districts with a focus on gender during weekdays and weekend. Lastly, the standard deviational ellipse (SDE) analysis is used to systematically analyze the orientation, direction, spatiotemporal expansion trends and the differences in check-in distribution in Guangzhou, China. The results of this study show that LBSN is a reliable source of data to observe human check-in behavior in space and time within a specified geographic area. Furthermore, it shows that female users are more likely to use social media as compared to male users. The human check-in behavior patterns for social media network usage by gender seems to be slightly different during weekdays and weekend.

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Analysis of product Twitter data though opinion mining

Cited by 15 publications

References 5 publications

Big Data Analysis to Observe Check-in Behavior Using Location-Based Social Media Data

Big Data Analysis to Observe Check-in Behavior Using Location-Based Social Media Data

Visualization, Spatiotemporal Patterns, and Directional Analysis of Urban Activities Using Geolocation Data Extracted from LBSN

Spatiotemporal Analysis to Observe Gender Based Check-In Behavior by Using Social Media Big Data: A Case Study of Guangzhou, China

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