TweetViz/Location Profiling

Profiling locations for business intelligence

We are developing methods for associating tweets with a business venue and then mining information from the tweets about the customers of the business, such as having negative sentiment about “slow wi-fi” and positive sentiment about “mint mojitos”. Our methods are based on tweet text, social network information, and the information in images posted in tweets. Customers are profiled in aggregate, such as whether they typically visit in groups or by themselves, or their hometown.

To help business owners, we have developed TweetViz, a prototype visual interface. With TweetViz, a business owner can select a business of interest and see the most popular items mentioned at the business, and the associated tweets, as well as a summary of simple demographic customer information.

More recently photo-centric social media sites have shown exceptional growth and become major platforms for interaction. The trend has given rise to web-scale multimedia posts, generally a combination of image, video, caption, check-in, or other metadata. Among them, captions usually act as a summary of the major content of a post to the audience. Moreover, captions make multimedia content more searchable and convey a richer semantic representation. Because captioning is driven by multiple contexts and various activities, it requires mining and correlating multi-modal contexts specific to the circumstances where they are applied. We are working on identifying useful contextual information and developing integrated models for automatically or semi-automatically captioning a variety of media content.

Related Publications

2016
Publication Details
  • SIGIR 2016
  • Jul 18, 2016

Abstract

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Social media offers potential opportunities for businesses to extract business intelligence. This paper presents Tweetviz, an interactive tool to help businesses extract actionable information from a large set of noisy Twitter messages. Tweetviz visualizes tweet sentiment of business locations, identifies other business venues that Twitter users visit, and estimates some simple demographics of the Twitter users frequenting a business. A user study to evaluate the system's ability indicates that Tweetviz can provide an overview of a business's issues and sentiment as well as information aiding users in creating customer profiles.
Publication Details
  • ICME 2016
  • Jul 11, 2016

Abstract

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Captions are a central component in image posts that communicate the background story behind photos. Captions can enhance the engagement with audiences and are therefore critical to campaigns or advertisement. Previous studies in image captioning either rely solely on image content or summarize multiple web documents related to image's location; both neglect users' activities. We propose business-aware latent topics as a new contextual cue for image captioning that represent user activities. The idea is to learn the typical activities of people who posted images from business venues with similar categories (e.g., fast food restaurants) to provide appropriate context for similar topics (e.g., burger) in new posts. User activities are modeled via a latent topic representation. In turn, the image captioning model can generate sentences that better reflect user activities at business venues. In our experiments, the business-aware latent topics are effective for adapting to captions to images captured in various businesses than the existing baselines. Moreover, they complement other contextual cues (image, time) in a multi-modal framework.

Social Media-Based Profiling of Business Locations

Publication Details
  • Fuji Xerox Technical Report
  • Mar 17, 2016

Abstract

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We present a method for profiling businesses at specific locations that is based on mining information from social media. The method matches geo-tagged tweets from Twitter against venues from Foursquare to identify the specific business mentioned in a tweet. By linking geo-coordinates to places, the tweets associated with a business, such as a store, can then be used to profile that business. From these venue-located tweets, we create sentiment profiles for each of the stores in a chain. We present the results as heat maps showing how sentiment differs across stores in the same chain and how some chains have more positive sentiment than other chains. We also estimate social group size from photos and create profiles of social group size for businesses. Sample heat maps of these results illustrate how the average social group size can vary across businesses.
Publication Details
  • AAAI
  • Feb 12, 2016

Abstract

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Image localization is important for marketing and recommendation of local business; however, the level of granularity is still a critical issue. Given a consumer photo and its rough GPS information, we are interested in extracting the fine-grained location information (i.e. business venues) of the image. To this end, we propose a novel framework for business venue recognition. The framework mainly contains three parts. First, business aware visual concept discovery: we mine a set of concepts that are useful for business venue recognition based on three guidelines including business-awareness, visually detectable, and discriminative power. Second, business-aware concept detection by convolutional neural networks (BA-CNN): we pro- pose a new network architecture that can extract semantic concept features from input image. Third, multimodal business venue recognition: we extend visually detected concepts to multimodal feature representations that allow a test image to be associated with business reviews and images from social media for business venue recognition. The experiments results show the visual concepts detected by BA-CNN can achieve up to 22.5% relative improvement for business venue recognition compared to the state-of-the-art convolutional neural network features. Experiments also show that by leveraging multimodal information from social media we can further boost the performance, especially in the case when the database images belonging to each business venue are scarce.
2015

Inferring Crowd-Sourced Venues for Tweets

Publication Details
  • IEEE BigData 2015
  • Oct 29, 2015

Abstract

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Knowing the geo-located venue of a tweet can facilitate better understanding of a user's geographic context, allowing apps to more precisely present information, recommend services, and target advertisements. However, due to privacy concerns, few users choose to enable geotagging of their tweets resulting in a small percentage of tweets being geotagged; furthermore, even if the geo-coordinates are available, the closest venue to the geo-location may be incorrect. In this paper, we present a method for providing a ranked list of geo-located venues for a non-geotagged tweet, which simultaneously indicates the venue name and the geo-location at a very fine-grained granularity. In our proposed method for Venue Inference for Tweets ({\VIT}), we construct a heterogeneous social network in order to analyze the embedded social relations, and leverage available but limited geographic data to estimate the geo-located venue of tweets. A single classifier is trained to predict the probability of a tweet and a geo-located venue being linked, rather than training a separate model for each venue. We examine the performance of four types of social relation features and three types of geographic features embedded in a social network when predicting whether a tweet and a venue are linked, with a best accuracy of over 88%. We use the classifier probability estimates to rank the predicted geo-located venues of a non-geotagged tweet from over 19k possibilities, and observed an average top-5 accuracy of 29%.
2014

Social Media-based Profiling of Store Locations

Publication Details
  • ACM Multimedia Workshop on Geotagging and Its Applications in Multimedia
  • Nov 2, 2014

Abstract

Close
We present a method for profiling businesses at specific locations that is based on mining information from social media. The method matches geo-tagged tweets from Twitter against venues from Foursquare to identify the specific business mentioned in a tweet. By linking geo-coordinates to places, the tweets associated with a business, such as a store, can then be used to profile that business. We used a sentiment estimator developed for tweets to create sentiment profiles of the stores in a chain, computing the average sentiment of tweets associated with each store. We present the results as heatmaps which show how sentiment differs across stores in the same chain and how some chains have more positive sentiment than other chains. We also created profiles of social group size for businesses and show sample heatmaps illustrating how the size of a social group can vary.
Publication Details
  • ICWSM (The 8th International AAAI Conference on Weblogs and Social Media)
  • Jun 1, 2014

Abstract

Close
A topic-independent sentiment model is commonly used to estimate sentiment in microblogs. But for movie and product reviews, domain adaptation has been shown to improve sentiment estimation performance. We investigated the utility of topic-dependent polarity estimation models for microblogs. We examined both a model trained on Twitter tweets containing a target keyword and a model trained on an enlarged set of tweets containing terms related to a topic. Comparing the performance of the topic-dependent models to a topic-independent model trained on a general sample of tweets, we noted that for some topics, topic-dependent models performed better. We then propose a method for predicting which topics are likely to have better sentiment estimation performance when a topic-dependent sentiment model is used.