we1s-collector

Archived

With the retirement of the Web Services Kit (WSK) API by LexisNexis around 2019-07, core features of the collecter are no longer functional. A new API – along with new document format – requires new connection, query code, and preprocessing code.

The collector is a system for searching, paging, and ingesting article data from new sources (with a current focus on sources from the Lexis-Nexis web-services kit API). Its interface to Lexis-Nexis is built on a fork of the Yale DH Lab “lexis-nexis-wsk,” a Python package with convenience wrappers for accessing Lexis Nexis WSK API.

It consists of wrappers and utilities for processing queries into collections of news article metadata, with a “bagify” feature to transform article content into word lists. Input may be a comma-delimited lists of query jobs; output is into WE1S schema JSON files (one per article), and may be batch-packaged into zip files. It comes with a command line batch-query interface, searchcmd.py, and a single-query interface, search.py.

This software was developed for WE1S (WhatEvery1Says) for studying collections of news articles.

Requires WSK API credentials issued by LexisNexis in order to submit queries.

Python 3-based. Uses the LexisNexis Web Services Kit (WSK) via the Yale DHLab lexis-nexis-wsk. Other requirements are listed in requirements.txt

local install

1. Download or git clone https://github.com/whatevery1says/we1s-collector.git

2. From directory, install required python 3 packages with:

   pip -r requirements.txt

3. Edit config/config.py and add credentials

run a query set

Run a query set with the built-in test file:

 searchcmd.py -q queries.csv

Here are the usage details from searchcmd.py --help:

WSK command line interface for WE1S (WhatEvery1Says)
usage examples:
    python searchcmd.py -o ../wskoutput -q queries.csv
    ./searchcmd.py -o ../wskoutput -q queries.csv

optional arguments:
  -h, --help            show this help message and exit
  -b, --bagify
  -o OUTPATH, --outpath OUTPATH
                        output path, e.g. "../output"
  -q QUERIES, --queries QUERIES
                        specify query file path, e.g. queries.csv
  -z, --zip             zip the json output

Query files are comma-separated-value (.csv) files with a header row and one query defined per row.

source_title,source_id,keyword_string,begin_date,end_date,result_filter
Chicago Daily Herald,163823,body(plural(humanities)) or hlead(plural(humanities)),2017-01-01,2017-01-31,humanities
BBC Monitoring: International Reports,10962,body(plural(humanities)) or hlead(plural(humanities)),2017-01-01,2017-01-31,humanities
The Guardian (London),138620,body(plural(arts)) or hlead(plural(arts)),2017-01-01,2017-01-15,the arts
TVEyes - BBC 1 Wales,402024,body(plural(humanities)) or hlead(plural(humanities)),2017-01-01,2017-01-31,humanities
Washington Post,8075,body(liberal PRE/1 plural(arts)) or hlead(liberal PRE/1 plural(arts)),2017-01-01,2017-01-31,liberal arts

The fields are:

• source_title A human readable label for the source / publication, to be used in file names and metadata.
• source_id
  The LexisNexis WSK source id number, to be used in the query.
• keyword_string
  The search query string. This string may include keywords and WSK search operators such as hlead(), plural(), PRE/1 et cetera.
• begin_date
  Beginning of the seach date range, in YYYY-MM-DD format.
• end_date
  End of the seach date range, in YYYY-MM-DD format.
• result_filter Optional post-processing filter string. Results which do not contain this literal string are returned, but filtered into the (no-exact-matches) result group.

The default query filename is queries.csv – you may edit the existing queries.csv or create new .csv files with the required header columns and process those files using the -q argument.

Docker

This repository comes with a Dockerfile for installing as a Docker container on a generic virtual machine running Debian Linux with Python 3.6. The container image may be built locally, or it may be from from a pre-built image available from Docker Hub.

adding credentials to Docker

Note that in order to submit queries, the container must have valid credentials for the WSK API. These can be added:

1. before a local build, by editing the config/config.py file before building the image
2. when launching a container, by passing --env arguments to the docker run command
3. after entering a running container, by editing the /app/config/config.py file inside the container.
4. by running the container in a swarm and defining and mounting credentials as “Docker secrets”.

For more details on these options, see: config/CONFIG_README.md

run from Docker local build

1. Download or git clone https://github.com/whatevery1says/we1s-collector.git

2. From directory, build the image:

   docker build we1s-collector .

3. Run a container from the image.

   docker run we1s-collector –name we1s-collector

4. Enter the shell of the running container

   docker exec -it we1s-collector /bin/bash

5. Run a test query set:

   cd /app searchcmd.py -q queries.csv

run from Docker remote image

1. Run a container from an image hosted on Docker Hub:

   docker run whatevery1says/we1s-collector:latest –name we1s-collector

2. Enter the shell of the running container

   docker exec -it we1s-collector /bin/bash

3. Run a test query set:

   cd /app searchcmd.py -q queries.csv