nlp / AnalyzeTweets / 0.1.13
0. TL;DR
This is a recipe for Analyzing Tweets. It combines twitter/RetrieveTweetsWithKeyword, nlp/SocialSentimentAnalysis, and nlp/LDA into a micro-service.
1. Introduction
Recipes are plug-n-play utilities that solve a specific problem in a novel way. These micro-services are comprised of one or more algorithms that allow users to quickly and easily get value from Algorithmia. They’re modular, and should be thought of as mini-products with obvious value.
This recipe is composed in the following way:
- The recipe is given a keyword by the user and retrieves relevant tweets from twitter using twitter/RetrieveTweetsWithKeyword.
- These relevant tweets are analyzed, labelled and sorted by nlp/SocialSentimentAnalysis.
- The top 20% (positive) tweets and the bottom 20% (negative) tweets are used to extract positive and negative topics using nlp/LDA.
- The corresponding positive & negative topics and tweets are returned.
Input:
- (Required): The query keyword/string
- (Required): Number of tweets
- (Required): Twitter API authentication keys
Output
- A list of positive topics (nlp/LDA output)
- A list of negative topics (nlp/LDA output)
- A list of all tweets
- A list of positive tweets
- A list of negative tweets
2. Query
The query keyword/string: The keyword that you're searching for on Twitter. (key = "query")
Example of a query keyword/string:
{
"query": "algorithmia"
}Number of tweets: The number of tweets you want to return from the search. (key = "numTweets")
Example of number of tweets:
{
"numTweets": 250
}3. Authentication
Twitter API authentication keys: The API keys that are necessary to access Twitter API service. You can get yours here. (key = "auth")
{
"auth": {
"app_key": "xxxxxxx",
"app_secret": "xxxxxxx",
"oauth_token": "xxxxxxx",
"oauth_token_secret": "xxxxxxx"
}
}4. Output
{
"posLDA": [{
"good": 5,
"blizzard": 9,
"service": 7,
"awesome": 6,
"comcastcareers": 6,
"comcast": 43,
"pretty": 6,
"weekend": 6
},
...,
{
"great": 16,
"outdoor": 9,
"ads": 9,
"show": 10,
"nra": 15,
"american": 9,
"comcast": 23,
"internet": 8
}]
}A list of negative topics: A list of topics extracted from the negative tweets using nlp/LDA. (key = "negLDA")
Example of a list of negative topics:
{
"negLDA": [{
"https://t.co": 6,
"cable": 5,
"comcastcares": 4,
"stuallard": 4,
"sportsnet": 5,
"comcast": 43,
"internet": 6,
"hate": 9
},
...,
{
"premiere": 6,
"nra": 7,
"gun": 11,
"remove": 7,
"prejudice": 6,
"zombies": 6,
"images": 7,
"l.a": 6
}]
}A list of all tweets: All of the tweets that returned from the search query. (key = "allTweets")
Example of a list of all tweets:
{
"allTweets": [
{
"text": "Here is the link to out of control on iTunes! It would mean a lot if you guys got it! Thanks! https://t.co/FUns7SLwnv",
"created_at": "Mon Jan 25 15:30:36 +0000 2016",
"tweet_url": "https://twitter.com/statuses/691644344814559232",
"overall_sentiment": 0.5826,
"positive_sentiment": 0.159,
"neutral_sentiment": 0.841,
"negative_sentiment": 0
},
...,
{
"text": "Apple 5se rumored to use A9/M9 chips https://t.co/3O8P2ftTpJ #tech #gadgets",
"created_at": "Mon Jan 25 15:30:27 +0000 2016",
"tweet_url": "https://twitter.com/statuses/691644307577540609",
"overall_sentiment": 0,
"positive_sentiment": 0,
"neutral_sentiment": 1,
"negative_sentiment": 0
}
]
}
A list of positive tweets: The most positive tweets from the twitter search query. (key = "posTweets")
Example of a list of positive tweets:
{
"posTweets": [
{
"text": "Need some music this morning, #AmazonPrime free music thx! AMZN Mobile LLC - Amazon Music with Prime Music - https://t.co/frF0j5szpV #iTunes",
"created_at": "Mon Jan 25 15:30:37 +0000 2016",
"tweet_url": "https://twitter.com/statuses/691644349805785088",
"overall_sentiment": 0.7777,
"positive_sentiment": 0.328,
"neutral_sentiment": 0.672,
"negative_sentiment": 0
},
...,
{
"text": "This cool new #app helps #PowerAfrica track #energy deals in #Africa: https://t.co/2sZEfKKzZQ Download @AppStore https://t.co/a24GSQnCkg",
"created_at": "Mon Jan 25 15:30:32 +0000 2016",
"tweet_url": "https://twitter.com/statuses/691644327836192768",
"overall_sentiment": 0.5994,
"positive_sentiment": 0.274,
"neutral_sentiment": 0.726,
"negative_sentiment": 0
}
]
}A list of negative tweets: The most negative tweets from the twitter search query. (key = "newTweets")
Example of a list of negative tweets:
{
"negTweets": [
{
"text": "RT @TotalTrafficCIN: Closed due to accident in #Cincinnati on Kellogg Ave at Apple Hl Rd #traffic https://t.co/oaARUhWVVr",
"created_at": "Mon Jan 25 15:30:36 +0000 2016",
"tweet_url": "https://twitter.com/statuses/691644345544527873",
"overall_sentiment": -0.4767,
"positive_sentiment": 0,
"neutral_sentiment": 0.838,
"negative_sentiment": 0.162
},
...,
{
"text": "@FSBThamesValley hi, not quite sure what is wrong as from iPad it says blocked yet i can follow u from my pc obviously Apple issue.",
"created_at": "Mon Jan 25 15:30:33 +0000 2016",
"tweet_url": "https://twitter.com/statuses/691644333779505152",
"overall_sentiment": -0.749,
"positive_sentiment": 0,
"neutral_sentiment": 0.73,
"negative_sentiment": 0.27
}
]
}5. Example
Example 1:
- Parameter 1: A query keyword/string
- Parameter 2: Number of tweets
- Parameter 3: Twitter API authentication keys
{
"query": "google",
"numTweets": 250,
"auth": {
"app_key": "xxxxxxx",
"app_secret": "xxxxxxx",
"oauth_token": "xxxxxxx",
"oauth_token_secret": "xxxxxxx"
}
}