def get_sentence_emotions(self, sentence: str) -> List[Emotion]:
"""
Get list of all emotions in a given sentence.
"""
sentence_emotions = []
negated_terms = get_negated_words(sentence)
for term in process_content(sentence,
self.process_params.terms_mapping):
for emotion in self.get_term_emotions(term,
is_negated=term
in negated_terms):
sentence_emotions.append(emotion)
return sentence_emotions
def _get_emotions_for_series_folder_fast(
folder_path: str,
limbic_model: LexiconLimbicModel,
output_file_path: Optional[str] = None
) -> Dict[int, Dict[int, List[TimeEmotion]]]:
"""
Given a folder (assuming all files have S*E* pattern), computes the
emotions for each episode of each season
If parameter "fast" is given as input, emotions will be computed with a
proxy to identify negation sentences
which might not guarantee to catch all linguistic negations. (e.g. "this is far from good").
"""
seasons_episodes_subtitles_emotions: Dict[int, Dict[
int, List[TimeEmotion]]] = defaultdict(lambda: defaultdict(list))
season_episode_time_words: Dict[int, Dict[int, Dict[
int, Set[Optional[str]]]]] = defaultdict(
lambda: defaultdict(lambda: defaultdict(set)))
season_episode_time_sentence: Dict[int, Dict[int, Dict[
int, List[str]]]] = defaultdict(
lambda: defaultdict(lambda: defaultdict(list)))
subtitle_words = set()
for srt_file in os.listdir(folder_path):
subtitles = load_subtitles(os.path.join(folder_path, srt_file))
season, episode = _get_season_episode(srt_file)
for sub in subtitles:
if _contains_negation(sub.content):
season_episode_time_sentence[season][episode][
sub.start.seconds].append(sub.content)
season_episode_time_words[season][episode][
sub.start.seconds].add(None)
else:
for term in process_content(sub.content,
limbic_model.terms_mapping):
subtitle_words.add(term)
season_episode_time_words[season][episode][
sub.start.seconds].add(term)
_update_seasons_emotions_data(season_episode_time_words,
seasons_episodes_subtitles_emotions,
season_episode_time_sentence, subtitle_words,
limbic_model)
if output_file_path:
_save_seasons_emotions_to_file(seasons_episodes_subtitles_emotions,
output_file_path)
return seasons_episodes_subtitles_emotions
def test_process_content_with_terms_mapping(self):
for input_test, expected_output in TEST_CASES.items():
output = process_content(input_test, terms_mapping=TERMS_MAPPING)
self.assertEqual(output, expected_output)
def test_process_content(self):
for input_test, expected_output in TEST_CASES.items():
output = process_content(input_test)
self.assertEqual(output, expected_output)