emotiq/api.py at master · breadchris/emotiq · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
import urllib, base64, requests, json, os
from textblob import TextBlob

def get_search_results(query):
    headers = {
        # Request headers
        'Ocp-Apim-Subscription-Key': os.environ['Ocp-Apim-Subscription-Key'],
    }

    params = urllib.urlencode({
        # Request parameters
        'q': query,
        'category': 'stocks',
        'count': '80',
        'offset': '0',
        'freshness': 'Month',
        'mkt': 'en-us',
        'safeSearch': 'Moderate',
    })

    r = requests.get('https://api.cognitive.microsoft.com/bing/v5.0/news/search?%s' % params, headers=headers)
    news_articles = json.loads(r.text)

    article_urls = []
    articles = []
    for article in news_articles["value"]:
        article_urls.append(article["url"])
        articles.append(article)

    article_thumbnails = []
    for article in news_articles["value"]:
        if 'image' in article.keys() and len(article_thumbnails) < 8:
            article_thumbnails.append([article["name"], article["url"], article["image"]["thumbnail"]["contentUrl"], article["description"]])

    return articles, article_thumbnails

def get_textblob_sentiment(text_list):
    sentiments = []
    for text in text_list:
        blob = TextBlob(text)
        for sentence in blob.sentences:
            sentiments.append(sentence.sentiment.polarity)

    return sentiments

def get_sentiment_scores(text_list):
    headers = {
        # Request headers
        'Content-Type': 'application/json',
        'Ocp-Apim-Subscription-Key': os.environ['Ocp-Apim-Subscription-Key'],
    }

    params = {
        'documents': []
    }

    for n, text in enumerate(text_list):

        text_info = {
            "language": "en",
            "id": str(n),
            "text": text

        }
        params['documents'].append(text_info)

    params = json.dumps(params)
    r = requests.post('https://westus.api.cognitive.microsoft.com/text/analytics/v2.0/sentiment', data=params, headers=headers)
    sentiments = json.loads(r.text)["documents"]
    return [x["score"] for x in sentiments]

def get_text_topics(text_list):
    headers = {
        # Request headers
        'Content-Type': 'application/json',
        'Ocp-Apim-Subscription-Key': os.environ['Ocp-Apim-Subscription-Key'],
    }

    params = {
        'documents': []
    }

    for n, text in enumerate(text_list):

        text_info = {
                "language": "en",
                "id": str(n),
                "text": text
            }
        params['documents'].append(text_info)

    params = json.dumps(params)
    r = requests.post('https://westus.api.cognitive.microsoft.com/text/analytics/v2.0/keyPhrases', data=params, headers=headers)
    sentiments = json.loads(r.text)["documents"]

    out = []
    for phrases in sentiments:
        out.extend(phrases["keyPhrases"])
    return out