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Customer Support on Twitter

Customer Support on Twitter

Stefan
Open Dataset
Creators: Axelbrooke, Stuart
Publication Date: 2017
Creators: Axelbrooke, Stuart

The Customer Support on Twitter dataset is a large, modern corpus of tweets and replies to aid innovation in natural language understanding and conversational models, and for study of modern customer support practices and impact. It is intended to facilitate advancements in natural language understanding and the development of conversational models. Compiled by Stuart Axelbrooke in 2017, this dataset encompasses tweets and replies from prominent companies such as Apple, Amazon, Uber, Delta, and Spotify. It provides valuable insights into contemporary customer support practices and their impact, making it an essential resource for researchers interested in automated response generation, sentiment analysis, and conversational flow modeling. The dataset is approximately 516.53 MB in size. It is designed for the analysis of conversation dynamics and contains several key attributes. Each tweet entry has a unique, anonymized tweet ID (tweet_id), an anonymized user ID (author_id), a timestamp (created_at), and the tweet text (text), where sensitive information such as phone numbers and email addresses has been masked to ensure privacy. It differentiates between inbound tweets (inbound), which are directed at companies by customers, and outbound tweets, which are responses from the companies. Additionally, in_response_to_tweet_id and response_tweet_id fields allow for the reconstruction of entire conversation threads by linking tweets to their respective responses.

Twitter Dataset

Twitter Dataset

Stefan
Open Dataset
Creators: Cheng, Zhiyuan; Caverlee, James; Lee, Kyumin
Publication Date: 2010
Creators: Cheng, Zhiyuan; Caverlee, James; Lee, Kyumin
This dataset is a collection of scraped public twitter updates used in coordination with an academic project to study the geolocation data related to twittering. We provide both training set and test set in the paper You Are Where You Tweet: A Content-Based Approach to Geo-locating Twitter Users in CIKM 2010. The training set contains 115,886 Twitter users and 3,844,612 updates from the users. All the locations of the users are self-labeled in United States in city-level granularity. The test set contains 5,136 Twitter users and 5,156,047 tweets from the users. In total, the dataset has a size of 30,0 kB. All the locations of users are uploaded from their smart phones with the form of “UT: Latitude,Longitude”. The Twitter activity is covered over a period of five months, from September 2009 to January 2010, offering a valuable temporal snapshot of user interactions and content generation during that time. Structurally, the dataset is divided into four text files. The training set users file (“training_set_users.txt”) contains user information in the format “UserIDtUserLocation”, and the training set tweets file (“training_set_tweets.txt”) stores tweets in the format “UserIDtTweetIDtTweettCreatedAt”. Similarly, the test set users file (“test_set_users.txt”) follows the same format as the training set users file, while the test set tweets file (“test_set_tweets.txt”) follows the same structure as the training set tweets file.
Twitter 2010 data set

Twitter 2010 data set

Stefan
Open Dataset
Creators: Lerman, Kristina; Ghosh, Rumi; Surachawala, Tawan
Publication Date: 2010
Creators: Lerman, Kristina; Ghosh, Rumi; Surachawala, Tawan

The Twitter 2010 dataset contains Twitter activity data, focusing on tweets containing URLs and the follower-followee relationships among users during October 2010. It is particularly valuable for studying information diffusion, social network structures, and user interaction patterns on Twitter. The dataset has a size of 21,5 kB and includes 2,859,764 tweets that contain URLs, offering insights into content-sharing behaviors, and 736,930 users who posted these tweets. Additionally, it features 36,743,448 follower-followee relationships, allowing for the reconstruction of the social graph of active users. Each tweet record contains metadata such as tweet ID, creation date, source device, in-reply-to information, and the user’s follower and followee counts.

Yelp Open Dataset

Yelp Open Dataset

Stefan
Open Dataset
Creators: Yelp, Inc.
Publication Date: 2015
Creators: Yelp, Inc.
The Yelp dataset offers a collection of real-world data from Yelp, intended for educational and academic purposes. It encompasses information about businesses, user reviews, photos, and check-ins, providing valuable insights into local commerce and consumer behavior. In total, this dataset contains 6.9M online reviews for 150k businesses and covers 11 metropolitan areas. It also includes more than 200,000 images related to the reviews. It has a compressed size of 4,9 GB and uncompressed 10,9 GB available in JSON files. The data consists of multiple sub datasets:

  1. Yelp Business data: Contains business data including location data, attributes, and categories.
  2. Yelp Review data: Contains full review text data including the user_id that wrote the review and the business_id the review is written for.
  3. Yelp User data: User data including the user’s friend mapping and all the metadata associated with the user.
  4. Yelp Checkin data: Checkins on a business.
  5. Yelp Tip data: Tips written by a user on a business. Tips are shorter than reviews and tend to convey quick suggestions.
  6. Yelp Photo data: Contains photo data including the caption and classification (one of “food”, “drink”, “menu”, “inside” or “outside”).

Available as JSON files, use can use it to teach students about databases, to learn NLP, or for sample production data while you learn how to make mobile apps.

 

Amazon product co-purchasing network metadata

Amazon product co-purchasing network metadata

Stefan
Open Dataset
Creators: Leskovec, Jure
Publication Date: 2006
Creators: Leskovec, Jure

The data was collected by crawling the Amazon website and contains product metadata and review information about 548,552 different products (Books, music CDs, DVDs and VHS video tapes). It is valuable for analyzing product relationships, customer behavior, and the dynamics of product co-purchasing networks. For each product the following information is available:

Title
Salesrank
List of similar products (that get co-purchased with the current product)
Detailed product categorization
Product reviews: time, customer, rating, number of votes, number of people that found the review helpful.

The data was collected in summer 2006. It has a size of 201 MB and structured into:

  • Product Metadata: Information such as product ID, ASIN, title, group, sales rank, similar products, and categories.​

  • Product Reviews: Details including review time, customer ID, rating, number of votes, and helpfulness votes.

Web data: Amazon movie reviews

Web data: Amazon movie reviews

Stefan
Open Dataset
Creators: McAuley, Julian; Leskovec, Jure
Publication Date: 2012
Creators: McAuley, Julian; Leskovec, Jure

This dataset is a collection of approximately 8 million movie reviews from Amazon, spanning over a decade up to October 2012. It is particularly valuable for analyzing consumer behavior, sentiment analysis, and the evolution of user expertise in online reviews. In total, the dataset has a size of 3,1 GB. Each review includes detailed information such as the product’s unique identifier (ASIN), user ID, profile name, helpfulness rating, score, time of review (in Unix time), summary, and the full text of the review.

The dataset is organized with each review capturing multiple attributes:​

  • Product Information: Including the product’s unique identifier (ASIN).​

  • User Information: Such as user ID and profile name.​

  • Review Details: Encompassing helpfulness rating, score, time of review, summary, and the full text.

 

Web data: Amazon Fine Foods reviews

Web data: Amazon Fine Foods reviews

Stefan
Open Dataset
Creators: McAuley, Julian; Leskovec, Jure
Publication Date: 2012
Creators: McAuley, Julian; Leskovec, Jure

This dataset consists of reviews of fine foods from amazon. The data is 116 MB ins size and spans a period of more than 10 years, including all ~500,000 reviews up to October 2012. Each review includes detailed information such as the product’s unique identifier (ASIN), user ID, profile name, helpfulness rating, score, time of review (in Unix time), summary, and the full text of the review. This dataset is particularly valuable for analyzing consumer behavior, sentiment analysis, and the evolution of user expertise in online reviews.

The dataset is organized with each review capturing multiple attributes:​

  • Product Information: Including the product’s unique identifier (ASIN).​

  • User Information: Such as user ID and profile name.​

  • Review Details: Encompassing helpfulness rating, score, time of review, summary, and the full text.

 

Google web graph

Google web graph

Stefan
Open Dataset
Creators: Leskovec, Jure; Lang, Kevin J.; Dasgupta, Anirban; Mahoney, Michael W.
Publication Date: 2002
Creators: Leskovec, Jure; Lang, Kevin J.; Dasgupta, Anirban; Mahoney, Michael W.

The Google Web Graph dataset offers a detailed representation of the web’s hyperlink structure as captured in 2002. In this dataset, nodes correspond to individual web pages, and directed edges represent hyperlinks from one page to another. This structure is particularly valuable for studying web connectivity, page importance algorithms like PageRank, and the overall topology of the internet during that period. In total, the dataset has a size of 0,02 GB and comprises 875,713 nodes and 5,105,039 directed edges, reflecting the extensive interlinking characteristic of the early 2000s web. Structurally, the dataset is presented as a single directed graph where each node represents a web page, and each directed edge denotes a hyperlink from one page to another. This format facilitates analyses of web page connectivity, identification of influential pages, and exploration of community structures within the web.

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