Sentiment

 from google.cloud import language

from google.cloud import language_v1

import six

import pandas as pd

data = pd.read_csv("all-data.csv", encoding="ISO-8859-1")

data.columns = ['sentiment', 'text']

data2 = data.columns[1]

#This format data.loc[1].at['text'] can be used to access the data at that element

def analyze_text_sentiment(text):

    client = language.LanguageServiceClient()

    document = language.Document(content=text, type_=language.Document.Type.PLAIN_TEXT)

    response = client.analyze_sentiment(document=document)

    sentiment = response.document_sentiment

    results = dict(

        text=text,

        score=f"{sentiment.score:.1%}",

        magnitude=f"{sentiment.magnitude:.1%}",

    )

    for k, v in results.items():

        print(f"{k:10}: {v}")

def sample_analyze_sentiment(content):

    client = language_v1.LanguageServiceClient()

    # content = 'Your text to analyze, e.g. Hello, world!'

    if isinstance(content, six.binary_type):

        content = content.decode("utf-8")

    type_ = language_v1.Document.Type.PLAIN_TEXT

    document = {"type_": type_, "content": content}

    response = client.analyze_sentiment(request={"document": document})

    sentiment = response.document_sentiment

    print("Score: {}".format(sentiment.score))

    print("Magnitude: {}".format(sentiment.magnitude))

text = "During the last quarter of 2016, the loans outpaced leases for the first time. Despite the significant market share, SolarCity’s solar panel installations during the first quarter declined around 39% year over year during the first quarter of 2017. The company has revealed sleek new solar roof panels that it claims are more efficient and cost-competitive compared to regular roofing products."

analyze_text_sentiment(text)

#for x in range(20):

#    val = data.loc[x].at['text']  #stores the data at that point in val

#    analyze_text_sentiment(val)

#text = "Guido van Rossum is great!"

#analyze_text_sentiment(text)

cat sentiment-out.txt 

SolarCity's Dominance in U.S. Residential Market

SolarCity commands a significant 41% share of the residential solar installation market, and the company also operates in the commercial and utilities space. The company's business model of offering to lease solar panels with no upfront costs has helped it gain a big slice of the residential solar market in the past.

Score: 0.30000001192092896, Magnitude: 0.699999988079071

However, the model has put a burden on the company to constantly raise capital from investors to cover the upfront cost of installing the leased solar systems. Following its purchase by Tesla late last year and owing to the solar market dynamics, the company has moved on to allow consumers to take out loans and own their solar arrays outright. It now puts SolarCity in line with practices of other industry players.

Score: -0.20000000298023224, Magnitude: 1.2000000476837158

During the last quarter of 2016, the loans outpaced leases for the first time. Despite the significant market share, SolarCity’s solar panel installations during the first quarter declined around 39% year over year during the first quarter of 2017. The company has revealed sleek new solar roof panels that it claims are more efficient and cost-competitive compared to regular roofing products.

Score: 0.20000000298023224, Magnitude: 1.2000000476837158

First Solar has done things differently since its inception and managed to cut its own road to success. Right from developing cadmium-telluride (CdTe) based panels to developing advanced grid integration, plant control, forecasting and energy scheduling capabilities, the company has been able to sustain and succeed in the highly competitive solar energy market for decades.

Score: 0.5, Magnitude: 1.100000023841858

First Solar is hopeful about its new-age Series 6 solar modules and is on course to ramp up the production of its new 400-watt-plus form factor, which will keep it competitive in the coming times, reports GTM. The company operates as a vertically integrated business, which puts it in control of everything starting from sourcing of raw materials to recycling of solar panels.

Score: 0.30000001192092896, Magnitude: 0.6000000238418579

admin@instance-2:/home/public/Sentiment-gcp-v1/file_/home/danteoconnor3$ cat version-2.py

from google.cloud import language

from google.cloud import language_v1

import six

import pandas as pd

# for python3

import sys

data = pd.read_excel("solar-city.xlsx")

data.columns = ['text']

def analyze_sentiment(content):

    client = language_v1.LanguageServiceClient()

    if isinstance(content, six.binary_type):

        content = content.decode("utf-8")

    type_ = language_v1.Document.Type.PLAIN_TEXT

    document = {"type_": type_, "content": content}

    response = client.analyze_sentiment(request={"document": document})

    sentiment = response.document_sentiment

    print("Score: {}, Magnitude: {}".format(sentiment.score, sentiment.magnitude))

    #print("Magnitude: {}".format(sentiment.magnitude))

print(data.shape)

with open('sentiment-out.txt', 'w') as sys.stdout:

    for x in range(32):

        val = data.loc[x].at['text']  #stores the data at that point in val

        print(val)

        analyze_sentiment(val)

        print('\n')   

#f= open("sentiment-out.txt","w+")

#for x in range(1,32):

#    val = data.loc[x].at['text']  #stores the data at that point in val

#    f.write(val)

#    f.write(str(analyze_sentiment(val)))

    #f.write(analyze_sentiment(val))

    #print('\n')