I am trying to create an app that will help users find restaurants/movie theaters/malls/etc. to hang out based on ratings and distance. Other than just the place itself, I would also like to know more detailed information about the place. For example, if I were to look for parks, I would also like to know if theres a basketball or tennis court there. Ratings and popularity would also be an important aspect to prioritize suggestions.
After looking through all three of the APIs, I could not really find any substantial differences other than their search limits. Could anyone really differentiate each API for me? Maybe even recommend one based on my specific need?
Thanks!
The Foursquare API would fit this use case perfectly because you can supply very specific filters through the API. Also, they have extensive coverage around the world, unlike Google or Yelp.
I would check out the venues/explore endpoint and use a categoryId of Parks. You can use a query parameter of "basketball" or "tennis" to find parks that have courts for these.
Related
I'm starting an online business targeted at a particular demographic and interests so I would like to produce content targeted at what this particular target market are actually searching for.
Google Ads allowed me to refine my target audience to the exact categories (demographics and interests) I needed but I couldn't tell me what that category of people tend to search for except for the tiny subset that happens to click on one of my ads which is very rare given I am just starting with a small budget. I would like to know the most popular search terms for everyone in the categories I specified not just those who happened to click on my ads.
I tried Google Trends, that told me the popularity of a particular search term for a given country but that's too broad - I need to narrow it down to a particular city, age group, parental status and interests. Google Trends also helped me find popular related search terms given a particular search term so I could try using that to see if there are any common popular related search terms related to my guesses but I could miss terms related to terms I never thought of.
I could try producing content across a rage of topics which I think my target audience might be interested in and then analyse the results using Google Ads but that could be a very expensive trial and error process and I might miss more popular topics which I never thought of.
Of course I could try to ask my target market in person directly (by interrupting people in the street!) but that would be very expensive for me because I would have to travel to and stay at the location where my online business is targeted, hoping to meet people with the exact same demographic and interests that I am looking.
I'm sure there must be a way to figure this out using the the Google search analytics. Essentially, all I need is a list of most popular recent Google search terms for a particular location, demographic and interests group in Google Analytics. Could anyone help me understand how to get this list?
Here are a few considerations, even if you found an answer.
Take a look at the AdRoll platform. Here's a potentially helpful article from them about target audience and demographics.
A recent article about AdWords demographic targeting. An older looking article, connecting demographics to search queries, but page's source code suggests an update this year.
Last but not least, you're probably eligible to talk with a Google Small Business Advisor.
I've proposed a title for our thesis, Movie Success Prediction through Social Media comments using Sentiment Analysis, is there a way you can get the comments on social media (twitter, Instagram, Facebook etc.) and use it for your software? like an API or any other way. is that even possible to use your software on different social media to get the comments for prediction or should i change my title and stick to one social media like Facebook or twitter only?
what's the good algorithm for this?
what programming language and framework/IDE should i use?
I've done lots of research on google and still hoping for more info here. Thank you.
Edit: I'll only use YouTube and YouTube API.
From the title of your question, it seems that the method you need to use is distant supervision. You need to retrieve data with labels you think it is proper for your task. For instance, a tweet containing #perfect hashtag would probably be a positive tweet. So, you can define set of hashtags for your task, negative, positive or even for neutral; then you can retrieve tweets by those via Twitter API. For your task, those should be for movies, therefore your data should contain movie related information in first place.
Given that you will deal with text data and you'd like to create your own dataset, it is better to start with Twitter. Its API works for your needs and it is very well-documented. The language and frameworks are upto your choice, since APIs supports many known languages as well. Personally, I'd start with python or java to quickly solve future problems easier with community support.
For a general survey of this area, you may dive into papers and resources from here:
https://scholar.google.com.tr/scholar?hl=en&q=distant+supervision+sentiment+analysis
Distant supervision could be used to create a sentiment lexicon out of millions English tweets by using sets of negative and positive hashtags as well. You may take a look at Chapter 5 of this thesis ( https://spectrum.library.concordia.ca/980377/1/Ozdemir_MCompSc_F2015.pdf ), this may also give a good insight for your thesis, too.
Hope this helps.
Cheers
In our project, we use search engine, but the result need to be ranked based on each user's interest, similar to recommendation according to users' keyword.
If we separate the two system, it would cost a lot time.
Is there a better way to combine Search Engine and Recommend System together?
Or is there a simple way to customize my ranking strategy to achieve this?
This is what we were trying to do in our project as well. There are two things while solving this problem - Relevancy vs Personalization. You should look at how much of personalization is ruining the relevancy of the query. For example, if I'm suggesting news, then it makes sense to suggest based on location. I hope you already would have analyzed the use cases.
The way that I followed was - after getting the results on the search, then re-rank results to give personal suggestions. For example if I was searching for a specific algorithm to code, then getting the result set and re-ranking on my preference, lets say on, Java (based on my previous history) will make sense. In any case relevancy is of utmost importance and then we fit in user's preferences.
Again the use case is important, if this was for a news search, then directly querying and retrieving on location is best way to do it.
This is my first time dabbling in NLP so please excuse my ignorance. I'm looking for a method to extract interests/likes/hobbies from users' social profiles. Here is an example where all the interests/likes/hobbies are in bold:
"I consider myself a pretty diverse character... I'm a professional
wrestler, but I'd take a bullet for Wall•E. I train like a one-man genocide machine in the gym, but I cried at
"Armageddon." I'll head bang to AC/DC, and I'm seriously
considering getting a Legend of Zelda tattoo. I'm 420-friendly. I
like to party it up with the frat crowd one night, hang out with
my Burning Man friends the next, play Halo and World of
Warcraft the next, and jam with friends that aren't any younger than
40 the next. My youngest friend is 16, my oldest friend is 66. I'll
sing karaoke at the bars, and I'm my friends' collective
psychiatrist/shoulder."
The profiles are plain text. There are no meta tags or ids associated with any of it, it's just a paragraph of text.
My naiive idea was to take each noun and match it against Freebase to see if it's an activity/artist/movie/book etc. The problem is that although most entities mentioned will be things the user likes, she will also mention things she doesn't like and I have no means of distinguishing the 2.
I have 2 questions:
What sub field of NLP should I be looking at? Some googleable algorithms/techniques/authors would be greatly appreciated.
How hard is this problem?
Thanks!
First, unless using NLP to do this is a particular objective for you, check your problem domain to see if you can avoid it completely.
For instance:
do these profiles have tags (supplied either by the Site or by the
user)?
what does the Site's API make available (assuming that's how you are accessing this data; if you are scraping it, then this doesn't of course apply)? A good example, Facebook. if you read a user's posts, you'll see words like "wrestler", "karaoke", etc. but if you look at what fields are exposed via the Graph API, you'll see that these activities nearly always have an associated FB ID.
I am not a specialist in this field, but I can recommend a couple of resources directed to NLP and which are accessible to the non-specialist or novice. The first is a text processing API. This simple web service uses REST and JSON IO. It is free and seems to have a fairly large rate limit.
This API appears to rely heavily on the excellent Natural Language Tooolkit (NLTK) which is a mature stable library in python, that includes modules directed to the problem in your Question, e.g., Sentiment Analysis, Tagging and Chunk Extraction, etc.
Which particular sub-domain is most relevant to solving the Question in the OP? I don't know, but I suspect there's a module somewhere in the NLTK that does what you need. Finding that module is hopefully just a matter of skimming the API Documentation (which is organized by module); reading the Getting Started section which contains an excellent survey of NLTK's modules as well as demos for all of each of them.
I'm working on a feature that uses the foursquare API to find venues nearby. Is startMonitoringSignificantLocationChanges accurate enough for this? I.e., does foursquare use startMonitoringSignificantLocationChanges or do they use startUpdatingLocation, or both? If both, how should you use them together?
Even after reading this data analisys of startMonitoringSignificantLocationChanges, I'm still not sure which to use?
Also, what about for an app like Grindr?
From looking at that article I would say no but it depends entirely on what you mean by nearby. Remember that in a shopping centre it is extremely easy for their to be numerous venues near you in a 250 - 500 metre radius. I mean quite a few shops are 10 metres wide or so, same with coffee shops etc. But then again it depends what you mean by venue.
Personally if I was making the app though I'd want something more predictable and accurate than the significant location change method. That seems to be geared more towards finding out a persons locale rather than finding what they are near too within said locale. For proper location services I'd still want to use the fully fledged GPS data.