Social Media Text Processing and Semantic Analysis for Smart Cities

by   João Filipe Figueiredo Pereira, et al.

With the rise of Social Media, people obtain and share information almost instantly on a 24/7 basis. Many research areas have tried to gain valuable insights from these large volumes of freely available user generated content. With the goal of extracting knowledge from social media streams that might be useful in the context of intelligent transportation systems and smart cities, we designed and developed a framework that provides functionalities for parallel collection of geo-located tweets from multiple pre-defined bounding boxes (cities or regions), including filtering of non-complying tweets, text pre-processing for Portuguese and English language, topic modeling, and transportation-specific text classifiers, as well as, aggregation and data visualization. We performed an exploratory data analysis of geo-located tweets in 5 different cities: Rio de Janeiro, São Paulo, New York City, London and Melbourne, comprising a total of more than 43 million tweets in a period of 3 months. Furthermore, we performed a large scale topic modelling comparison between Rio de Janeiro and São Paulo. Interestingly, most of the topics are shared between both cities which despite being in the same country are considered very different regarding population, economy and lifestyle. We take advantage of recent developments in word embeddings and train such representations from the collections of geo-located tweets. We then use a combination of bag-of-embeddings and traditional bag-of-words to train travel-related classifiers in both Portuguese and English to filter travel-related content from non-related. We created specific gold-standard data to perform empirical evaluation of the resulting classifiers. Results are in line with research work in other application areas by showing the robustness of using word embeddings to learn word similarities that bag-of-words is not able to capture.



There are no comments yet.


page 1

page 26


Linking Tweets with Monolingual and Cross-Lingual News using Transformed Word Embeddings

Social media platforms have grown into an important medium to spread inf...

Word Embeddings to Enhance Twitter Gang Member Profile Identification

Gang affiliates have joined the masses who use social media to share tho...

Deriving Disinformation Insights from Geolocalized Twitter Callouts

This paper demonstrates a two-stage method for deriving insights from so...

From Topic Networks to Distributed Cognitive Maps: Zipfian Topic Universes in the Area of Volunteered Geographic Information

Are nearby places (e.g. cities) described by related words? In this arti...

Learning Class-specific Word Representations for Early Detection of Hoaxes in Social Media

As people increasingly use social media as a source for news consumption...

Characterization of citizens using word2vec and latent topic analysis in a large set of tweets

With the increasing use of the Internet and mobile devices, social netwo...

Learning from #Barcelona Instagram data what Locals and Tourists post about its Neighbourhoods

Massive tourism is becoming a big problem for some cities, such as Barce...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.