I used Tweepy, the Twitter library for Python, to look at Tweets based containing specific keywords, and applying a date and location range. This process was relatively simple.
Now I would like to use python to search for Facebook posts in a similar fashion, yet facebook's data and API don't seem to allow me to 1) search for posts 2) search with multiple criteria. Is there a way to do this, or is it simply a lost cause and I should stick to using Twitter?
Related
I'm new to Python and am very confused on how to begin this assignment:
Write Python code to connect to Twitter search API at:
https://api.twitter.com/1.1/search/tweets.json
with at least the following parameters:
q for a search topic of your interest;
count to 100 for 100 records
to retrieve twitter data and assign the data to a variable.
I already created the app to access Twitter's API. Thanks.
I had this same issue. According to Twitter, you must register an account to even use basic search features of public tweets and there is no way around it. I followed the guide for the complete setup here and can confirm it works
Edit: the article is for php, but changing it to python shouldn’t be very difficult. It has all the methods outlined in the article
Just as a hint, you might want to use tweepy for this. They have very good documentation and its much easier than trying to reinvent the wheel.
I would like to develop a system in python to get the position and number of the results of keywords in google.
I tried using the Google API, however, I found that with the use of custom search engine and I have different results with fewer pages compared to "official" google. I've tried a variety of ready-made modules (eg: xgoogle, pattern, etc.).
I tried scraping but I do not think is the right way.
My question is: what is the best approach to achieve the results required by me without scraping? It is not possible to have them?
Always use web services APIs. If a site does not have one, and you are not the site's owner, then that is an indication that they do not want you to use automatic tools to fetch their data.
Additionally, fetching a keyword rank is dubious at best. Google adjusts its rank on many factors, including your search history, your location, your locale, and any number of factors that are trade secrets. Using the API will be the most generic results, even if they don't match the ones you get when you search from Firefox or Chrome.
TL;DR Your best bet are the methods you already don't like.
I'm currently extracting data from DBpedia articles using a SPARQLWrapper for python, but I can't seem to find how to extract the number of watchers (and other statistical information) for a given article.
Is there an easy way to achieve this? I don't mind if it's through DBpedia, or directly through wikipedia (using wget, for example).
Thanks for any advice.
It shell be prohibited to get the number of watchers for every arbitrary article, as it is considered to be a security leak if everyone could find unwatched pages. For example, only privileged users have access to Special:Unwatched Pages. There is a toolserver tool (which has access to the DB) showing the number of watchers, but it is restricted to pages with more than 30 watchers for the same reasons - at least unauthenticated.
The MediaWiki query API exposes only mostly content and status information about articles, though you can query and evaluate the public logs or revision histories as well to get statistical data about (public) user actions. For more stats about the Wikimedia sites you may have a look at Meta:Statistics, where various data sources (mostly http://stats.wikimedia.org/) and visualisations of them are listed.
We are looking to add a news/articles section to an existing site which will be powered by aggregating content via RSS feeds. The requirements are
Be able to aggregate lots of feeds. Initially we will start with small number of and eventually we may be aggregating few hundreds of them.
We don't want to display the whole post on our site. We will display summary or short description and when user clicks on read more, he will be taken to the original post on external site.
We would like to grab the image/s related to a post and display that as a small thumbnail with a post on our site.
Create an automated tag cloud out of all the aggregated content.
Categorize aggregated content by using category/sub-category structure.
The aggregation piece should perform well.
Our web app is built using Django and so I am looking into selecting one the following packages. Based on our requirements, which package would you recommend?
django-planet
django-news
planetplanet
feedjack
If you have a good idea of what you want, why not just try them all? If you have pretty strict requirements, write it yourself, roll your own aggregator with feedparser.
I have little working knowledge of python. I know that there is something called a Twitter search API, but I'm not really sure what I'm doing. I know what I need to do:
I need point data for a class. I thought I would just pull up a map of the world in a GIS application, select cities that have x population or larger, then export those selections to a new table. That table would have a key and city name.
next i randomly select 100 of those cities. Then I perform a search of a certain term (in this case, Gaddafi) for each of those 100 cities. All I need to know is how many posts there were on a certain day (or over a few days depending on amount of tweets there were).
I just have a feeling there is something that already exsists that does this, and I'm having a hard time finding it. I've dowloaded and installed python-twitter but have no idea how to get this search done. Anyone know where I can find or how I can make this tool? Any suggestions would really help. Thanks!
A tweet itself comes with a geo tag. But it is a new feature and majority tweets do not have it. So it is not possible to search for all tweets containing "Gaddafi" from a city given the city name.
What you could do is the reverse, you search for "Gaddafi" first (regardless of geo location), using search api. Then, for each tweet, find the location of the poster (either thru the RESTful api or use some sort of web scraping).
so basically you can classify the tweets collected according to the location of the poster.
I think only tweepy have access to both twitter search API as well as RESTful API.