| """ |
| @author: Edward R Jones |
| @version 1.34 |
| @copyright 2020 - Edward R Jones, all rights reserved. |
| """ |
|
|
| import sys, random, string, re |
| import warnings |
| import numpy as np |
| import pandas as pd |
|
|
| from collections import Counter |
| from copy import deepcopy |
|
|
| import matplotlib.pyplot as plt |
| import matplotlib.pylab as pylab |
|
|
| |
| from nltk import pos_tag |
| from nltk.tokenize import word_tokenize |
| from nltk.stem.snowball import SnowballStemmer |
| from nltk.stem import WordNetLemmatizer |
| from nltk.corpus import wordnet as wn |
| from nltk.corpus import stopwords |
| |
| |
| from wordcloud import WordCloud, STOPWORDS |
|
|
| class text_analysis(object): |
| def __init__(self, synonyms=None, stop_words=None, pos=True, stem=True): |
| if synonyms!=None and type(synonyms) != dict: |
| raise RuntimeError("\n"+\ |
| "***Invalid TextAnalytics Initialization.\n"+\ |
| "***synonyms are invalid, expecting 'Dictionary'.") |
| sys.exit() |
| if stop_words!=None and type(stop_words) != list: |
| raise RuntimeError("\n"+\ |
| "***Invalid TextAnalytics Initialization.\n"+\ |
| "***stop words are invalid, expecting 'List'.") |
| sys.exit() |
| if pos!=True and pos!=False: |
| raise RuntimeError("\n"+\ |
| "***Invalid TextAnalytics Initialization.\n"+\ |
| "***POS is not True or False") |
| sys.exit() |
| if stem!=True and stem!=False: |
| raise RuntimeError("\n"+\ |
| "***Invalid TextAnalytics Initialization.\n"+\ |
| "***STEM is not True or False") |
| sys.exit() |
| if synonyms==None: |
| self.synonyms_ = {} |
| else: |
| self.synonyms_ = synonyms |
| if stop_words==None: |
| self.stop_words_ = [] |
| else: |
| self.stop_words_ = stop_words |
| self.pos_ = pos |
| self.stem_ = stem |
| |
| def preprocessor(s): |
| |
| s = s.lower() |
| |
| punc = string.punctuation |
| for i in range(len(punc)): |
| s = s.replace(punc[i], ' ') |
| for i in range(10): |
| j = str(i) |
| s = s.replace(j, " ") |
| s = sentiment_analysis.reduce_lengthening(s) |
| return s |
| |
| |
| def analyzer(self, s): |
| |
| syns = { 'wont':'would not', "'nt": "n't", |
| 'cant':'can not', 'cannot':'can not', |
| 'couldnt':'could not', |
| 'shouldnt':'should not', |
| 'wouldnt':'would not', |
| 'dont': 'do not', |
| 'didnt': 'did not', |
| 'doesnt': 'does not', |
| 'havent': 'have not', |
| 'hadnt': 'had not', |
| } |
| syns.update(self.synonyms_) |
| |
| |
| s = s.lower() |
| |
| s = s.replace("'nt", "not") |
| s = s.replace("n't", " not") |
| punc = string.punctuation |
| for i in range(len(punc)): |
| s = s.replace(punc[i], ' ') |
| |
| |
| tokens = word_tokenize(s) |
| |
| tokens = [word for word in tokens if ('*' not in word) and \ |
| ("''" != word) and ("``" != word) and \ |
| (word!='description') and (word !='dtype') \ |
| and (word != 'object') and (word!="'s")] |
| |
| |
| for i in range(len(tokens)): |
| if tokens[i] in syns: |
| tokens[i] = syns[tokens[i]] |
| |
| |
| punctuation = list(string.punctuation)+['..', '...'] |
| pronouns = ['i', 'he', 'she', 'it', 'him', 'they', 'we', |
| 'us', 'them'] |
| others = ["'d", "co", "ed", "put", "say", "get", "can", "become", |
| "los", "sta", "la", "use", "iii", "else", "could", |
| "would", "come", "take", 'do', 'did', 'have', |
| 'should', 'had'] |
| stop = stopwords.words('english') + \ |
| punctuation + pronouns + others + self.stop_words_ |
| |
| filtered_terms = [word for word in tokens if (word not in stop) and \ |
| (len(word)>1) and (not word.replace('.','',1).isnumeric()) \ |
| and (not word.replace("'",'',2).isnumeric())] |
| |
| |
| |
| if self.pos_ == True or self.stem_ == True: |
| tagged_tokens = pos_tag(filtered_terms, lang='eng') |
| else: |
| tagged_tokens = filtered_terms |
| |
| if self.stem_ == True: |
| stemmer = SnowballStemmer("english") |
| wn_tags = {'N':wn.NOUN, 'J':wn.ADJ, 'V':wn.VERB, 'R':wn.ADV} |
| wnl = WordNetLemmatizer() |
| stemmed_tokens = [] |
| for tagged_token in tagged_tokens: |
| term = tagged_token[0] |
| pos = tagged_token[1] |
| pos = pos[0] |
| try: |
| pos = wn_tags[pos] |
| z = wnl.lemmatize(term, pos=pos) |
| if z not in stop: |
| stemmed_tokens.append(z) |
| except: |
| z = stemmer.stem(term) |
| if z not in stop: |
| stemmed_tokens.append(z) |
| else: |
| stemmed_tokens = tagged_tokens |
| return stemmed_tokens |
| |
| def score_topics(u, scores=True, display=True): |
| topics = True |
| if topics==False and scores==False: |
| return None |
| n_reviews = u.shape[0] |
| if n_reviews <= 0: |
| print("Number of reviews is zero") |
| sys.exit() |
| n_topics = u.shape[1] |
| if n_topics <= 0: |
| print("Number of topics is zero") |
| sys.exit() |
| doc_prob = np.array([0.0]*n_reviews, dtype=float) |
| doc_topic = np.array([0]*n_reviews, dtype=int) |
| for i in range(n_reviews): |
| doc_prob[i] = u[i].max() |
| for j in range(n_topics): |
| if u[i][j] == doc_prob[i]: |
| doc_topic[i] = j |
| continue |
| z = np.asarray([doc_topic, doc_prob]) |
| z = z.T |
| df_prob = pd.DataFrame(doc_prob.T, columns=['prob']) |
| df_topic = pd.DataFrame(doc_topic.T, columns=['topic']) |
| df = df_topic.join(df_prob) |
| |
| if display==True: |
| df0 = df.groupby('topic').count() |
| df0 = df0.rename(columns={'prob':'N'}) |
| df0.index = df0.index.astype('int') |
| print("\n ***Topic Counts***\n") |
| print(" Topic N P") |
| |
| df0 = df0.assign(P=0) |
| for t in (range(n_topics)): |
| x = df0['N'].iloc[t]*100/n_reviews |
| df0.loc[t,'P'] = x |
| |
| df0.plot.bar(y='P', xlim=(0, n_topics-1), fontsize=14) |
| |
| for t in (range(n_topics)): |
| print(" {:<5d}{:>5d}{:>7.1f}%".format(t, \ |
| df0['N'].iloc[t], df0['P'].iloc[t])) |
| if scores==False: |
| return df_topic |
| else: |
| return df |
|
|
| def display_topics(uv, terms, n_terms=15, \ |
| word_cloud=False, mask=None): |
| for topic_idx, topic in enumerate(uv.components_): |
| message = "Topic #%d: " %(topic_idx+1) |
| print(message) |
| abs_topic = abs(topic) |
| if type(terms[0])==tuple: |
| topic_terms_sorted = \ |
| [[terms[i][0], topic[i]] \ |
| for i in abs_topic.argsort()[:-n_terms - 1:-1]] |
| else: |
| topic_terms_sorted = \ |
| [[terms[i], topic[i]] \ |
| for i in abs_topic.argsort()[:-n_terms - 1:-1]] |
| |
| k = 5 |
| n = int(n_terms/k) |
| m = n_terms - k*n |
| for j in range(n): |
| l = k*j |
| message = '' |
| for i in range(k): |
| if topic_terms_sorted[i+l][1]>0: |
| word = "+"+topic_terms_sorted[i+l][0] |
| else: |
| word = "-"+topic_terms_sorted[i+l][0] |
| message += '{:<15s}'.format(word) |
| print(message) |
| if m> 0: |
| l = k*n |
| message = '' |
| for i in range(m): |
| if topic_terms_sorted[i+l][1]>0: |
| word = "+"+topic_terms_sorted[i+l][0] |
| else: |
| word = "-"+topic_terms_sorted[i+l][0] |
| message += '{:<15s}'.format(word) |
| print(message) |
| print("") |
| if word_cloud: |
| topic_cloud = {} |
| for i in range(n_terms): |
| topic_cloud[topic_terms_sorted[i][0]] = \ |
| topic_terms_sorted[i][1] |
| |
| text_plot.word_cloud_dic(topic_cloud, mask=mask, |
| max_words=n_terms) |
| return |
| |
| def display_term_frequency(tf, terms, n_tail=20, tfidf=None, |
| word_cloud=True, zipf_plot=True): |
| td = text_plot.term_dic(tf, terms, scores=None) |
| |
| k = Counter(td) |
| sorted_terms = k.most_common() |
| n_terms = len(sorted_terms) |
| doc_terms = {} |
| tfidf_score = {} |
| for i in range(n_terms): |
| |
| doc_terms[terms[i]] = tf[:,i].count_nonzero() |
| if tfidf != None: |
| for i in range(n_terms): |
| tfidf_score[terms[i]] = tfidf[:,i].sum() |
| |
| k = Counter(td) |
| tail_terms = k.most_common(n_tail) |
| print("") |
| print(n_tail, "MOST COMMON TERMS") |
| if tfidf == None: |
| print("---------------------------------------") |
| print("{:.<15s}{:>12s}{:>12s}".format('Term', 'Term Freq.', |
| 'Doc Freq.')) |
| for t in tail_terms: |
| |
| print("{:.<15s}{:>9d}{:>12d}".format(t[0], t[1], doc_terms[t[0]])) |
| print("---------------------------------------\n") |
| else: |
| print("------------------------------------------------") |
| print("{:.<15s}{:>12s}{:>12s}{:>9s}".format('Term', 'Term Freq.', |
| 'Doc Freq.', 'TFIDF')) |
| for t in tail_terms: |
| |
| print("{:.<15s}{:>9d}{:>12d}{:>12.1f}".format(t[0], t[1], |
| doc_terms[t[0]], |
| tfidf_score[t[0]])) |
| print("------------------------------------------------\n") |
| |
| bot_terms = k.most_common()[-n_tail:] |
| print(n_tail, "LEAST COMMON TERMS") |
| if tfidf == None: |
| print("---------------------------------------") |
| print("{:.<15s}{:>12s}{:>12s}".format('Term', 'Term Freq.', |
| 'Doc Freq.')) |
| for t in bot_terms: |
| |
| print("{:.<15s}{:>9d}{:>12d}".format(t[0], t[1], doc_terms[t[0]])) |
| print("---------------------------------------\n") |
| else: |
| print("------------------------------------------------") |
| print("{:.<15s}{:>12s}{:>12s}{:>9s}".format('Term', 'Term Freq.', |
| 'Doc Freq.', 'TFIDF')) |
| for t in bot_terms: |
| |
| print("{:.<15s}{:>9d}{:>12d}{:>12.1f}".format(t[0], t[1], |
| doc_terms[t[0]], |
| tfidf_score[t[0]])) |
| print("------------------------------------------------\n") |
| |
| if word_cloud: |
| |
| text_plot.word_cloud_dic(td, mask=None, max_words=n_tail, |
| bg_color="maroon", size=(400,200), |
| random=12345) |
| print("") |
| |
| if zipf_plot==True: |
| |
| freq = np.zeros(n_terms) |
| i = 0 |
| for t in sorted_terms: |
| freq [i] = t[1] |
| i += 1 |
| plt.figure(figsize=(9,4)) |
| plt.title('Log ZIPF Plot') |
| plt.xlabel("Rank") |
| plt.ylabel("Log(Term Frequency)") |
| plt.yscale('log') |
| plt.title('Log Zipf Plot') |
| plt.grid(True) |
| ax = plt.gca() |
| ax.set_facecolor('steelblue') |
| plt.plot(freq, '-', color='gold', linewidth=3) |
| plt.show() |
| |
| x = np.zeros(n_tail).astype(str) |
| y = np.zeros(n_tail) |
| i = 0 |
| for t in tail_terms: |
| x[i] = t[0] |
| y[i] = t[1] |
| i+=1 |
| plt.figure(figsize=(9,4)) |
| plt.title('Term Frequency for Most Common Terms') |
| plt.grid(True) |
| plt.yscale('log') |
| plt.ylabel("Log(Term Frequency)") |
| plt.xticks(rotation=45, ha='right') |
| ax = plt.gca() |
| ax.set_facecolor('steelblue') |
| plt.bar(x, y, color='gold') |
| plt.show() |
| return |
|
|
| class text_plot(object): |
| |
| def shades_of_gray(word, font_size, position, orientation, |
| random_state=None, **kwargs): |
| return "hsl(0, 0%%, %d%%)" % random.randint(60,200) |
| |
| def word_cloud_string(s, mask=None, bg_color="maroon", |
| stopwords=None, max_words=30, random=12345, |
| size=(400, 200)): |
| try: |
| wcloud = WordCloud(background_color=bg_color, |
| mask=mask, max_words=max_words, stopwords=stopwords, |
| max_font_size=40, prefer_horizontal=0.9, |
| min_font_size=10, relative_scaling=0.5, |
| width=size[0], height=size[1], scale=1, margin=10, |
| random_state=random) |
| except NameError as err: |
| msg = "\nWord Cloud Package Not Installed\n"+\ |
| "Install using: CONDA INSTALL WORDCLOUD\n"+\ |
| "Call to word_cloud_string failed.\n" |
| print(err, msg) |
| sys.exit() |
| |
| wcloud.generate(s) |
| plt.imshow( \ |
| wcloud.recolor(color_func=text_plot.shades_of_gray, \ |
| random_state=random), interpolation="bilinear") |
| plt.axis("off") |
| plt.figure() |
| plt.show() |
| return |
| |
| def word_cloud_dic(td, mask=None, bg_color="maroon", |
| max_words=30, random=12345, size=(400, 200)): |
| try: |
| wcloud = WordCloud(background_color=bg_color, |
| mask=mask, max_words=max_words, |
| max_font_size=40, prefer_horizontal=0.9, |
| min_font_size=10, relative_scaling=0.5, |
| width=size[0], height=size[1], scale=1, margin=10, |
| random_state=random) |
| except NameError as err: |
| msg = "\nWord Cloud Package Not Installed\n"+\ |
| "Install using: CONDA INSTALL WORDCLOUD\n"+\ |
| "Call to word_cloud_dic failed.\n" |
| print(err, msg) |
| sys.exit() |
| |
| wcloud.generate_from_frequencies(td) |
| plt.imshow( \ |
| wcloud.recolor(color_func=text_plot.shades_of_gray, \ |
| random_state=random), interpolation="bilinear") |
| plt.axis("off") |
| plt.figure() |
| plt.show() |
| return |
| |
| |
| |
| |
| |
| def term_dic(tf, terms, scores=None): |
| td = {} |
| for i in range(tf.shape[0]): |
| |
| term_list = tf[i].nonzero()[1] |
| if len(term_list)>0: |
| if scores==None: |
| for t in np.nditer(term_list): |
| if td.get(terms[t]) == None: |
| td[terms[t]] = tf[i,t] |
| else: |
| td[terms[t]] += tf[i,t] |
| else: |
| for t in np.nditer(term_list): |
| score = scores.get(terms[t]) |
| if score != None: |
| |
| score_weight = abs(scores[terms[t]]) |
| if td.get(terms[t]) == None: |
| td[terms[t]] = tf[i,t] * score_weight |
| else: |
| td[terms[t]] += tf[i,t] * score_weight |
| return td |
| |
| |
| class sentiment_analysis(object): |
| |
| def __init__(self, sentiment_dic=None, n_terms=4): |
| self.n_terms=n_terms |
| self.sentiment_dic = {} |
| if sentiment_dic==None or sentiment_dic.lower()=='afinn': |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| dic={ |
| "abandon": -2, "abandoned": -2, "abandons": -2, |
| "abducted": -2, "abduction": -2, "abductions": -2, |
| "abhor": -3, "abhorred": -3, "abhorrent": -3, |
| "abhors": -3, "abilities": 2, "ability": 2, |
| "aboard": 1, "aborted": -1, "aborts": -1, |
| "absentee": -1, "absentees": -1, "absolve": 2, |
| "absolved": 2, "absolves": 2, "absolving": 2, |
| "absorbed": 1, "abuse": -3, "abused": -3, |
| "abuses": -3, "abusing": -3, "abusive": -3, |
| "accept": 1, "acceptable": 1, "acceptance": 1, |
| "accepted": 1, "accepting": 1, "accepts": 1, |
| "accessible": 1, "accident": -2, "accidental": -2, |
| "accidentally": -2, "accidents": -2, "acclaim": 2, |
| "acclaimed": 2, "accolade": 2, "accomplish": 2, |
| "accomplished": 2, "accomplishes": 2, "accomplishment": 2, |
| "accomplishments": 2, "accusation": -2, "accusations": -2, |
| "accuse": -2, "accused": -2, "accuses": -2, |
| "accusing": -2, "ache": -2, "achievable": 1, |
| "aching": -2, "acquit": 2, "acquits": 2, |
| "acquitted": 2, "acquitting": 2, "acrimonious": -3, |
| "active": 1, "adequate": 1, "admire": 3, |
| "admired": 3, "admires": 3, "admiring": 3, |
| "admit": -1, "admits": -1, "admitted": -1, |
| "admonish": -2, "admonished": -2, "adopt": 1, |
| "adopts": 1, "adorable": 3, "adoration": 3, |
| "adore": 3, "adored": 3, "adores": 3, |
| "adoring": 3, "adoringly": 3, "advanced": 1, |
| "advantage": 2, "advantageous": 2, "advantageously": 2, |
| "advantages": 2, "adventure": 2, "adventures": 2, |
| "adventurous": 2, "adversary": -1, "advisable": 1, |
| "affected": -1, "affection": 3, "affectionate": 3, |
| "affectionateness": 3, "afflicted": -1, "affordable": 2, |
| "affronted": -1, "aficionados": 2, "afraid": -2, |
| "aggravate": -2, "aggravated": -2, "aggravates": -2, |
| "aggravating": -2, "aggression": -2, "aggressions": -2, |
| "aggressive": -2, "aggressiveness": -2, "aghast": -2, |
| "agog": 2, "agonise": -3, "agonised": -3, |
| "agonises": -3, "agonising": -3, "agonize": -3, |
| "agonized": -3, "agonizes": -3, "agonizing": -3, |
| "agree": 1, "agreeable": 2, "agreed": 1, |
| "agreement": 1, "agrees": 1, "alarm": -2, |
| "alarmed": -2, "alarmist": -2, "alarmists": -2, |
| "alas": -1, "alert": -1, "alienation": -2, |
| "alive": 1, "allegation": -2, "allegations": -2, |
| "allergic": -2, "allow": 1, "ally": 2, |
| "alone": -2, "altruistic": 2, "amaze": 2, |
| "amazed": 2, "amazes": 2, "amazing": 4, |
| "ambitious": 2, "ambivalent": -1, "amicable": 2, |
| "amuse": 3, "amused": 3, "amusement": 3, |
| "amusements": 3, "anger": -3, "angered": -3, |
| "angers": -3, "angry": -3, "anguish": -3, |
| "anguished": -3, "animosity": -2, "annoy": -2, |
| "annoyance": -2, "annoyed": -2, "annoying": -2, |
| "annoys": -2, "antagonistic": -2, "anti": -1, |
| "anticipation": 1, "anxiety": -2, "anxious": -2, |
| "apathetic": -3, "apathy": -3, "apeshit": -3, |
| "apocalyptic": -2, "apologise": -1, "apologised": -1, |
| "apologises": -1, "apologising": -1, "apologize": -1, |
| "apologized": -1, "apologizes": -1, "apologizing": -1, |
| "apology": -1, "appalled": -2, "appalling": -2, |
| "appealing": 2, "appease": 2, "appeased": 2, |
| "appeases": 2, "appeasing": 2, "applaud": 2, |
| "applauded": 2, "applauding": 2, "applauds": 2, |
| "applause": 2, "appreciate": 2, "appreciated": 2, |
| "appreciates": 2, "appreciating": 2, "appreciation": 2, |
| "apprehensive": -2, "appropriate": 2, "appropriately": 2, |
| "approval": 2, "approved": 2, "approves": 2, |
| "ardent": 1, "arrest": -2, "arrested": -3, |
| "arrests": -2, "arrogant": -2, "arsehole": -4, |
| "ashame": -2, "ashamed": -2, "ass": -4, |
| "assassination": -3, "assassinations": -3, "assault": -2, |
| "assaults": -2, "asset": 2, "assets": 2, |
| "assfucking": -4, "asshole": -4, "astonished": 2, |
| "astound": 3, "astounded": 3, "astounding": 3, |
| "astoundingly": 3, "astounds": 3, "atrocious": -3, |
| "atrocity": -3, "attack": -1, "attacked": -1, |
| "attacking": -1, "attacks": -1, "attract": 1, |
| "attracted": 1, "attracting": 2, "attraction": 2, |
| "attractions": 2, "attractive": 2, "attractively": 2, |
| "attractiveness": 2, "attracts": 1, "audacious": 3, |
| "aura": 1, "authority": 1, "avenge": -2, |
| "avenged": -2, "avenger": -2, "avengers": -2, |
| "avenges": -2, "avenging": -2, "avert": -1, |
| "averted": -1, "averts": -1, "avid": 2, |
| "avoid": -1, "avoided": -1, "avoids": -1, |
| "await": -1, "awaited": -1, "awaits": -1, |
| "award": 3, "awarded": 3, "awards": 3, |
| "awesome": 4, "awful": -3, "awkward": -2, |
| "axe": -1, "axed": -1, "backed": 1, |
| "backing": 2, "backs": 1, "bad": -3, |
| "bad luck": -2, "badass": -3, "badly": -3, |
| "badness": -3, "bailout": -2, "balanced": 1, |
| "bamboozle": -2, "bamboozled": -2, "bamboozles": -2, |
| "ban": -2, "banish": -1, "bankrupt": -3, |
| "bankruptcy": -3, "bankster": -3, "banned": -2, |
| "barbarian": -2, "barbaric": -2, "barbarous": -2, |
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| "validates": 1, "validating": 1, "vapid": -2, |
| "verdict": -1, "verdicts": -1, "vested": 1, |
| "vexation": -2, "vexing": -2, "vibrant": 3, |
| "vicious": -2, "victim": -3, "victimization": -3, |
| "victimize": -3, "victimized": -3, "victimizes": -3, |
| "victimizing": -3, "victims": -3, "victor": 3, |
| "victors": 3, "victory": 3, "victories": 3, |
| "vigilant": 3, "vigor": 3, "vile": -3, |
| "vindicate": 2, "vindicated": 2, "vindicates": 2, |
| "vindicating": 2, "violate": -2, "violated": -2, |
| "violates": -2, "violating": -2, "violation": -2, |
| "violations": -2, "violence": -3, "violence-related": -3, |
| "violent": -3, "violently": -3, "virtuous": 2, |
| "virulent": -2, "vision": 1, "visionary": 3, |
| "visioning": 1, "visions": 1, "vitality": 3, |
| "vitamin": 1, "vitriolic": -3, "vivacious": 3, |
| "vividly": 2, "vociferous": -1, "vomit": -3, |
| "vomited": -3, "vomiting": -3, "vomits": -3, |
| "vulnerability": -2, "vulnerable": -2, "walkout": -2, |
| "walkouts": -2, "wanker": -3, "want": 1, |
| "war": -2, "warfare": -2, "warm": 1, |
| "warmhearted": 2, "warmness": 2, "warmth": 2, |
| "warn": -2, "warned": -2, "warning": -3, |
| "warnings": -3, "warns": -2, "waste": -1, |
| "wasted": -2, "wasting": -2, "wavering": -1, |
| "weak": -2, "weakened": -2, "weakness": -2, |
| "weaknesses": -2, "wealth": 3, "wealthier": 2, |
| "wealthy": 2, "weary": -2, "weep": -2, |
| "weeping": -2, "weird": -2, "welcome": 2, |
| "welcomed": 2, "welcomes": 2, "well-being": 2, |
| "well-championed": 3, "well-developed": 2, "well-established": 2, |
| "well-focused": 2, "well-groomed": 2, "well-proportioned": 2, |
| "whimsical": 1, "whitewash": -3, "whore": -4, |
| "wicked": -2, "widowed": -1, "willingness": 2, |
| "win": 4, "winner": 4, "winning": 4, |
| "wins": 4, "winwin": 3, "wisdom": 1, |
| "wish": 1, "wishes": 1, "wishing": 1, |
| "withdrawal": -3, "wits": 2, "woebegone": -2, |
| "woeful": -3, "won": 3, "wonderful": 4, |
| "wonderfully": 4, "woo": 3, "woohoo": 3, |
| "wooo": 4, "woow": 4, "worn": -1, |
| "worried": -3, "worries": -3, "worry": -3, |
| "worrying": -3, "worse": -3, "worsen": -3, |
| "worsened": -3, "worsening": -3, "worsens": -3, |
| "worshiped": 3, "worst": -3, "worth": 2, |
| "worthless": -2, "worthy": 2, "wow": 4, |
| "wowow": 4, "wowww": 4, "wrathful": -3, |
| "wreck": -2, "wrenching": -2, "wrong": -2, |
| "wrongdoing": -2, "wrongdoings": -2, "wronged": -2, |
| "wrongful": -2, "wrongfully": -2, "wrongly": -2, |
| "wtf": -4, "wtff": -4, "wtfff": -4, |
| "xo": 3, "xoxo": 3, "xoxoxo": 4, |
| "xoxoxoxo": 4, "yeah": 1, "yearning": 1, |
| "yeees": 2, "yes": 1, "youthful": 2, |
| "yucky": -2, "yummy": 3, "zealot": -2, |
| "zealots": -2, "zealous": 2 |
| } |
| |
| for k, v in dic.items(): |
| self.sentiment_dic[k] = v |
| |
| if k != "no" and k != "fun": |
| self.sentiment_dic["no " + k] = -1*v |
| |
| |
| if k != "not" and k != "good" and k != "working": |
| self.sentiment_dic["not " + k] = -1*v |
| else: |
| self.sentiment_dic = sentiment_dic |
| self.sentiment_word_dic = deepcopy(self.sentiment_dic) |
| i = 0 |
| for term in self.sentiment_word_dic: |
| self.sentiment_word_dic[term] = i |
| i += 1 |
| |
| def preprocess(s): |
| |
| s = s.lower() |
| |
| s = s.replace("'nt", "n't") |
| s = s.replace("n't", " not") |
| punc = string.punctuation |
| for i in range(len(punc)): |
| s = s.replace(punc[i], ' ') |
| return s |
| |
| def preprocessor(s): |
| |
| s = s.lower() |
| |
| s = s.replace("'nt", " not") |
| s = s.replace("n't", " not") |
| s = s.replace("'s", " is") |
| |
| punc = string.punctuation |
| for i in range(len(punc)): |
| s = s.replace(punc[i], ' ') |
| for i in range(10): |
| j = str(i) |
| s = s.replace(j, " ") |
| s = sentiment_analysis.reduce_lengthening(s) |
| return s |
| |
| def reduce_lengthening(text): |
| pattern = re.compile(r"(.)\1{2,}") |
| return pattern.sub(r"\1\1", text) |
|
|
| |
| |
| def analyzer(self, s): |
| |
| |
| s = sentiment_analysis.preprocessor(s) |
| |
| |
| tokens = word_tokenize(s) |
| |
| tokens = [word for word in tokens if ('*' not in word) and \ |
| ("''" != word) and ("``" != word) and \ |
| (word!='description') and (word !='dtype') \ |
| and (word != 'object') and (word!="'s")] |
| |
| |
| """ |
| for i in range(len(tokens)): |
| if tokens[i] in syns: |
| tokens[i] = syns[tokens[i]] |
| """ |
| |
| |
| punctuation = list(string.punctuation)+['..', '...', '....'] |
| pronouns = ['i', 'he', 'she', 'him', 'they', 'we', 'us', |
| 'this', 'them'] |
| others = ["'d", "co", "ed", "put", "say", "get", "can", "become", |
| "los", "sta", "la", "use", "iii", "else", "could", |
| "would", "should", "come", "take", "be", "a", "have"] |
| stop = punctuation + pronouns + others |
| filtered_terms = [word for word in tokens |
| if (word not in stop) and |
| (len(word)>1) and |
| (not word.replace('.','',1).isnumeric()) and |
| (not word.replace("'",'',2).isnumeric())] |
| for word in filtered_terms: |
| word = sentiment_analysis.reduce_lengthening(word) |
| flist = [] |
| i = 0 |
| while i < len(filtered_terms): |
| if filtered_terms[i] != 'not' and filtered_terms[i] != 'no': |
| flist.append(filtered_terms[i]) |
| else: |
| |
| j = i+1 |
| if j < len(filtered_terms): |
| noWord = filtered_terms[i]+' '+filtered_terms[j] |
| |
| if self.sentiment_dic.get(noWord)==None: |
| |
| |
| if filtered_terms[i] == 'no': |
| flist.append(filtered_terms[i]) |
| else: |
| |
| flist.append(noWord) |
| i = j |
| i += 1 |
| return flist |
| |
| def scores(self, tf, terms): |
| |
| min_sentiment = +5.0 |
| max_sentiment = -5.0 |
| avg_sentiment = 0.0 |
| self.min_list, self.max_list = [],[] |
| n_reviews = tf.shape[0] |
| sentiment_score = [0.0]*n_reviews |
| n_sw = [0]*n_reviews |
| for i in range(n_reviews): |
| |
| n_sw[i] = 0 |
| term_list = tf[i].nonzero()[1] |
| if len(term_list)>0: |
| for t in np.nditer(term_list): |
| score = self.sentiment_dic.get(terms[t]) |
| if score != None: |
| sentiment_score[i] += score * tf[i,t] |
| n_sw[i] += tf[i,t] |
| if n_sw[i]>0: |
| sentiment_score[i] = sentiment_score[i]/n_sw[i] |
| if sentiment_score[i]==max_sentiment and n_sw[i]>=self.n_terms: |
| self.max_list.append(i) |
| if sentiment_score[i]>max_sentiment and n_sw[i]>=self.n_terms: |
| max_sentiment=sentiment_score[i] |
| self.max_list = [i] |
| |
| if sentiment_score[i]==min_sentiment and n_sw[i]>=self.n_terms: |
| self.min_list.append(i) |
| if sentiment_score[i]<min_sentiment and n_sw[i]>=self.n_terms: |
| min_sentiment=sentiment_score[i] |
| self.min_list = [i] |
| avg_sentiment += sentiment_score[i] |
| avg_sentiment = avg_sentiment/n_reviews |
| print("\nCorpus Average Sentiment: ", avg_sentiment) |
| print("\nMost Negative with", \ |
| self.n_terms, "or more Sentiment Words:") |
| for i in range(len(self.min_list)): |
| print("{:<s}{:<d}{:<s}{:<5.2f}".format(" Review ", \ |
| self.min_list[i], " Sentiment is ", min_sentiment)) |
| |
| print("\nMost Positive with", \ |
| self.n_terms, "or more Sentiment Words:") |
| for i in range(len(self.max_list)): |
| print("{:<s}{:<d}{:<s}{:<5.2f}".format(" Review ", \ |
| self.max_list[i], " Sentiment is ", max_sentiment)) |
| |
| dfs = pd.DataFrame(sentiment_score, columns=['sentiment']) |
| dfn = pd.DataFrame(n_sw, columns=['n_words']) |
| dfs = dfs.join(dfn) |
| return dfs |
