def home(request):
context = {}
context['query'] = False
context['search_query'] = ''
if request.method == 'GET':
return render(request, 'hue/home.html', context)
if 'search_q' in request.POST:
search_query = request.POST['search_q']
context['search_query'] = search_query
print(search_query)
context['query'] = True
# twitter.twitter_query(search_query)
r = RedditParser()
r.reddit_query(search_query, 25, 25)
path = os.path.realpath(os.path.abspath(os.path.join(os.path.split(inspect.getfile(inspect.currentframe() ))[0],"datumbox")))
ifile = cmd_subfolder + '/data.json'
ofile = path + '/sentiment.csv'
print ifile
print ofile
sentiment.analyze_sentiment(ifile, ofile, 0.1)
path = os.path.realpath(os.path.abspath(os.path.join(os.path.split(inspect.getfile(inspect.currentframe() ))[0],"semantic-similarity-master")))
cofile = path + '/reddit_senti.json'
os.system(path + "/similar" + ' ' + ifile + ' ' + ofile + ' ' + cofile)
with open(cofile) as data_file:
data = json.load(data_file)
context['data'] = json.dumps(data)
return render(request, 'hue/home.html', context)
def discord_response(user, message_contents):
"""Returns the result of detect intent with texts as inputs.
Using the same `session_id` between requests allows continuation
of the conversation."""
session_path = f"{agent}/sessions/{session_id}"
#print(f"Session path: {session_path}\n")
client_options = None
agent_components = AgentsClient.parse_agent_path(agent)
location_id = agent_components["location"]
if location_id != "global":
api_endpoint = f"{location_id}-dialogflow.googleapis.com:443"
print(f"API Endpoint: {api_endpoint}\n")
client_options = {"api_endpoint": api_endpoint}
session_client = SessionsClient(client_options=client_options)
input_string = message_contents
image_float = analyze_sentiment(user, input_string)
text_input = session.TextInput(text=input_string)
query_input = session.QueryInput(text=text_input, language_code=language_code)
request = session.DetectIntentRequest(
session=session_path, query_input=query_input
)
response = session_client.detect_intent(request=request)
#print("=" * 20)
#print(f"Query text: {response.query_result.text}")
response_messages = [
" ".join(msg.text.text) for msg in response.query_result.response_messages
]
return ((os.getcwd() + find_emotion_gif(image_float)), f"{' '.join(response_messages)}\n")
def review_highlights_(reviews):
reviews_texts = " ".join([r['text'] for r in reviews])
review_sentences = mining.to_sentences(reviews_texts)
#pre-process text and find features
review_words = mining.to_words(amazon_reviews)
review_words = mining.pos_tag(review_words)
review_words = [w for w in review_words if w[0]
not in mining.stopwords()]
review_bigrams = mining.find_bigram_collocations(review_words)
review_features = mining.amazon_features_from_collocations(
review_bigrams)
#find sentences that contain our features, summarize sentence groups
#and score features for sentiment
feature_sentences = {}
feature_sentiments = {f:0 for f in amazon_features}
for f in amazon_features:
sentences = [s for s in review_sentences if f in s]
cleaned_sentences = [mining.remove_stopwords(mining.to_words(s)) for s in sentences]
#pick most central sentence
central = summarize.rank_by_centrality(cleaned_sentences)
feature_sentences[f] = sentences[central[0]]
feature_sentiments[f] += sum([sentiment.analyze_sentiment(s,
amazon_features)[f] for s in sentences])
content = {f:(feature_sentences[f], feature_sentiments[f])}
return content
def run_app():
all_articles = load_articles(
'generated_data/recent_political_articles.json')
sentiments: typing.Dict[str, dict] = {}
print("Examining sentiments...")
for article in all_articles:
if article['document_type'] != 'article':
continue
article_path = 'generated_data/articles/%s.html' % str(
article['_id'])[len("nyt://article/"):]
print("Examining: %s" % article['headline']['main'])
sentiment = analyze_sentiment(
extract_article_text(article_path)).document_sentiment
sentiments[article['_id']] = {
'score': sentiment.score,
'magnitude': sentiment.magnitude
}
print("Saving sentiments...")
with open('generated_data/article_sentiments.json', 'w',
encoding='utf-8') as sentiments_file:
json.dump(sentiments,
sentiments_file,
sort_keys=True,
indent=4,
ensure_ascii=False)
print("Done!")
def detect_intent(user): #, agent, session_id, language_code
"""Returns the result of detect intent with texts as inputs.
Using the same `session_id` between requests allows continuation
of the conversation."""
session_path = f"{agent}/sessions/{session_id}"
print(f"Session path: {session_path}\n")
client_options = None
agent_components = AgentsClient.parse_agent_path(agent)
location_id = agent_components["location"]
if location_id != "global":
api_endpoint = f"{location_id}-dialogflow.googleapis.com:443"
print(f"API Endpoint: {api_endpoint}\n")
client_options = {"api_endpoint": api_endpoint}
session_client = SessionsClient(client_options=client_options)
input_string = input("Enter your prompt for bitberg")
while(input_string != 'close'):
image_float = analyze_sentiment(user, input_string)
text_input = session.TextInput(text=input_string)
query_input = session.QueryInput(text=text_input, language_code=language_code)
request = session.DetectIntentRequest(
session=session_path, query_input=query_input
)
response = session_client.detect_intent(request=request)
#display image somehow
#
#
### display_gif(find_emotion_gif(image_float))
#
#
if -1.0 < image_float < -0.5:
__display_gif('angry')
#display angry
elif image_float < 0.0:
__display_gif('sad')
#display sad
elif image_float < 0.5:
__display_gif('bored')
#display bored
else:
__display_gif('happy')
#display happy
print("=" * 20)
print(f"Query text: {response.query_result.text}")
response_messages = [
" ".join(msg.text.text) for msg in response.query_result.response_messages
]
print(f"Response text: {' '.join(response_messages)}\n")
input_string = input()
def addComment(comment):
# BASE_URL = "http://localhost:3000/comments"
isDict = type(comment) == dict
# awards = ''
if (isDict):
text = comment['body']
id = comment['id']
time = comment['created_utc']
score = comment['score']
parent_post = comment['link_id']
parent_comment = "t_0 " if comment[
'parent_id'] == parent_post else comment['parent_id']
# if parent_post.startswith("t"):
# parent_post = parent_post[3:]
author = comment['author']
else:
text = comment.body
time = comment.created_utc
id = comment.id
score = comment.score
parent_post = comment.link_id[3:]
# parent_comment = "top_level" if comment.link_id[3:] == comment.parent_id[3:] else comment.parent_id[3:]
parent_comment = "t_0" if comment.link_id == comment.parent_id else comment.parent_id
author = comment.author
# awards = item.all_awardings
r = requests.get(url=BASE_URL + "/id/" + id)
sentiment = s.analyze_sentiment(text)
data = {
"comment_id": id,
"comment_date": time,
"ticker": "TEST",
"parent_post": parent_post,
"parent_comment": parent_comment,
"body": text,
"score": score,
"sentiment": sentiment,
"author": author
}
r = requests.post(url=BASE_URL + "/comments", data=data)
f.write(text)
if __name__ == "__main__":
download()
convert()
csv_folder = "csv"
if not os.path.exists(csv_folder):
os.makedirs(csv_folder)
txts = glob.glob("text/*.txt")
for i, txtfile in enumerate(txts):
author, results = sentiment.analyze_sentiment(txtfile)
fname = author.replace(" ", "") # Remove spaces
np.savetxt(
f"{csv_folder}/{fname}.csv",
results,
delimiter=",",
header="window #, polarity, subjectivity",
)
print(f"Data saved to {csv_folder}/{fname}.csv [{i+1}/{len(txts)}]")
print("\nDone!")