def extract_claims(raw_claim):
for claim in split_into_sentences(raw_claim):
if POINTLESS_CLAIM_RE.match(claim):
continue
# if sentence starts with "brand" or "it", remove it, and
# capitalize the next letter
claim = ltrim_sentence(claim, ('brand', 'it', 'however'))
yield claim
def scrape_company(url, known_brands):
soup = scrape_soup(url)
company = strip_company(
soup.select('#company_score_company_title h1')[0].text)
c = dict(company=company)
# rating
score = int(soup.select('#company_score_score')[0].text.strip())
r = dict(company=company, score=score, max_score=MAX_SCORE)
status_path = soup.select('#company_score_status img')[0]['src']
r['description'], r['judgment'] = scrape_description_and_judgment(
status_path)
r['url'] = url
# icon
icon_as = soup.select('#company_score_company_icon a')
if icon_as:
icon_a = icon_as[0]
c['url'] = icon_a['href']
c['logo_url'] = urljoin(url, icon_a.img['src'])
# sector
for a in soup.select('#breadcrumbs a'):
if 'sectors' in a['href']:
c['category'] = a.text
break # ignore "Industry Innovators" category
# match up brands to logos
brands = sorted(known_brands[company])
sb2b = dict((smunch(b), b) for b in brands)
for img in soup.select('span.brand_icon img'):
logo_url = urljoin(url, img['src'])
sb = smunch(img['alt'])
sb = SMUNCHED_BRAND_CORRECTIONS.get(sb, sb)
if sb in sb2b:
brand = sb2b[sb]
yield 'brand', dict(
company=company, brand=brand, logo_url=logo_url)
else:
log.warn(u'No matching brand for {} ({}: {})'.format(
repr(img['alt']), company, u', '.join(brands)))
# match twitter handles to company/brand
sc = smunch(company)
sbs = sorted(sb2b)
sb2th = {}
twitter_handles = [
a.text.strip() for a in
soup.select('#company_score_action_right a')]
def match_twitter_handle(th):
if th in IGNORE_TWITTER_HANDLES:
return
sth = smunch(th[1:])
for i in range(len(sth), 1, -1):
if (not th.endswith('Brand') and sth[:i] == sc[:i] and
'twitter_handle' not in c):
c['twitter_handle'] = th
return
for sb in sbs:
if sth[:i] == sb[:i] and sb not in sb2th:
sb2th[sb] = th
return
else:
if 'twitter_handle' not in c:
c['twitter_handle'] = th
else:
log.warn(u'No matching brand/company for {} ({}: {})'.format(
repr(th), company, u', '.join(brands)))
for th in twitter_handles:
match_twitter_handle(th)
for sb, th in sb2th.iteritems():
brand = sb2b[sb]
yield 'brand', dict(company=company, brand=brand, twitter_handle=th)
yield 'company', c
yield 'rating', r
# parse claims
for b in soup.find(id='company_score').parent.select('b'):
m = SUBSCORE_RE.match(b.text)
if m and isinstance(b.next_sibling, unicode):
# used this for debugging
#area = m.group(1)
area_score = int(m.group(2))
area_max_score = int(m.group(3))
raw_claim = b.next_sibling
if NO_SPLIT_CLAIM_RE.match(raw_claim):
claims = [raw_claim]
else:
claims = list(split_into_sentences(raw_claim))
for claim in claims:
# strip company name off claim
claim = ltrim_sentence(claim, [company, 'the company'])
judgment = claim_to_judgment(claim)
# if score is low, maybe it's not so positive after all
if judgment == 1 and area_score / area_max_score < 0.5:
judgment == 0
yield 'claim', dict(company=company,
claim=claim,
judgment=judgment)
