Analyze Tweets

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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 A list of positive topics: A list of topics extracted from the positive tweets using nlp/LDA. (key = "posLDA") Example of a list of positive topics: {
 "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": [{
 "": 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!",
 "created_at": "Mon Jan 25 15:30:36 +0000 2016",
 "tweet_url": "",
 "overall_sentiment": 0.5826,
 "positive_sentiment": 0.159,
 "neutral_sentiment": 0.841,
 "negative_sentiment": 0
 "text": "Apple 5se rumored to use A9/M9 chips #tech #gadgets",
 "created_at": "Mon Jan 25 15:30:27 +0000 2016",
 "tweet_url": "",
 "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 - #iTunes",
 "created_at": "Mon Jan 25 15:30:37 +0000 2016",
 "tweet_url": "",
 "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: Download @AppStore",
 "created_at": "Mon Jan 25 15:30:32 +0000 2016",
 "tweet_url": "",
 "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",
 "created_at": "Mon Jan 25 15:30:36 +0000 2016",
 "tweet_url": "",
 "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": "",
 "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"

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curl -X POST -d '{{input | formatInput:"curl"}}' -H 'Content-Type: application/json' -H 'Authorization: Simple YOUR_API_KEY'
View cURL Docs
algo auth
algo run algo://nlp/AnalyzeTweets/0.1.10 -d '{{input | formatInput:"cli"}}'
View CLI Docs
import (
  algorithmia ""

input := {{input | formatInput:"go"}}

var client = algorithmia.NewClient("YOUR_API_KEY", "")
algo, _ := client.Algo("algo://nlp/AnalyzeTweets/0.1.10")
resp, _ := algo.Pipe(input)
response := resp.(*algorithmia.AlgoResponse)
View Go Docs
import com.algorithmia.*;
import com.algorithmia.algo.*;

String input = "{{input | formatInput:"java"}}";
AlgorithmiaClient client = Algorithmia.client("YOUR_API_KEY");
Algorithm algo = client.algo("algo://nlp/AnalyzeTweets/0.1.10");
AlgoResponse result = algo.pipeJson(input);
View Java Docs
import com.algorithmia._
import com.algorithmia.algo._

val input = {{input | formatInput:"scala"}}
val client = Algorithmia.client("YOUR_API_KEY")
val algo = client.algo("algo://nlp/AnalyzeTweets/0.1.10")
val result = algo.pipeJson(input)
View Scala Docs
var input = {{input | formatInput:"javascript"}};
           .then(function(output) {
View Javascript Docs
using Algorithmia;

var input = "{{input | formatInput:"cs"}}";
var client = new Client("YOUR_API_KEY");
var algorithm = client.algo("algo://nlp/AnalyzeTweets/0.1.10");
var response = algorithm.pipe<object>(input);
View .NET/C# Docs
var input = {{input | formatInput:"javascript"}};
           .then(function(response) {
View NodeJS Docs
import Algorithmia

input = {{input | formatInput:"python"}}
client = Algorithmia.client('YOUR_API_KEY')
algo = client.algo('nlp/AnalyzeTweets/0.1.10')
print algo.pipe(input)
View Python Docs

input <- {{input | formatInput:"r"}}
client <- getAlgorithmiaClient("YOUR_API_KEY")
algo <- client$algo("nlp/AnalyzeTweets/0.1.10")
result <- algo$pipe(input)$result
View R Docs
require 'algorithmia'

input = {{input | formatInput:"ruby"}}
client = Algorithmia.client('YOUR_API_KEY')
algo = client.algo('nlp/AnalyzeTweets/0.1.10')
puts algo.pipe(input).result
View Ruby Docs
use algorithmia::Algorithmia;

let input = {{input | formatInput:"rust"}};
let client = Algorithmia::client("YOUR_API_KEY");
let algo = client.algo("nlp/AnalyzeTweets/0.1.10");
let response = algo.pipe(input);
View Rust Docs
import Algorithmia

let input = "{{input | formatInput:"swift"}}";
let client = Algorithmia.client(simpleKey: "YOUR_API_KEY")
let algo = client.algo(algoUri: "nlp/AnalyzeTweets/0.1.10") { resp, error in
View Swift Docs
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