def __init__(self, post, author = "", title = ""):

self.author = author

self.title = title

def __init__(self, namesScore = 0, religionScore = 0, weaponryScore = 0, governmentScore = 0, outputsWordsArray = 0):

self.outputsWordsArray = outputsWordsArray

self.namesScore = namesScore

self.religionScore = religionScore

self.weaponryScore = weaponryScore

outputWordsArr = []

namesCount, religionCount, weaponryCount, governmentCount, wordCount = 0, 0, 0, 0, 0

for i, blog in enumerate(blogList):

scores = {}

wordCount = 0

print("Top words in document {}".format(i + 1))

for word in blog.words:

flag = True

word = word.lower() # Everything is in lowercase.

for punc in terms.punctuation():

if punc in word:

flag = False

wordCount+=1

if flag:

scores[word] = tfidf.tfidf(word, blog, blogList) # run tfidf

if word in terms.governmentTerms(): # increment count based on content to find word densities.

governmentCount+=1

if word in terms.weaponsTerms():

weaponryCount+=1

if word in terms.femaleNames() or word in terms.maleNames():

namesCount+=1

if word in terms.religiousTerms():

religionCount+=1

sorted_words = sorted(scores.items(), key=lambda x: x[1], reverse=True) # sort the words

for word, score in sorted_words[0:10]:

print("\tWord: {}, TF-IDF: {}".format(word, round(score, 5)))

outputWordsArr.append((word, round(score, 10)))

print("---------------------------------------------------------")

# Gathering the density scores of each of these defined features, and creating the returning data type

analysisOutputs = AnalysisObject(namesCount/wordCount,religionCount/wordCount,weaponryCount/wordCount,governmentCount/wordCount,outputWordsArr)

# Check if a word that is part of the different terms and also in the top-words list. That increases its weight.

weights = {"names": 2.0, "religion": 5.0, "weaponry": 8.0, "government": 15.0}

for upperKey in features:

for lowerKey in features[upperKey]:

if lowerKey != "words":

weightedFeatures[upperKey][lowerKey] = features[upperKey][lowerKey] * weights[lowerKey]

# pdb.set_trace()

# Get word densities of the new blog

namesCount, religionCount, weaponryCount, governmentCount, wordCount = 0, 0, 0, 0, 0

for word in tb(blog):

wordCount += 1

if word in terms.governmentTerms(): # increment count based on content to find word densities.

governmentCount += 1

if word in terms.weaponsTerms():

weaponryCount += 1

if word in terms.femaleNames() or word in terms.maleNames():

namesCount += 1

if word in terms.religiousTerms():

religionCount += 1

analysisOutputs = AnalysisObject(namesCount/wordCount,religionCount/wordCount,weaponryCount/wordCount,governmentCount/wordCount, None)

# Compare to the analyzed ones.

scores = {"good": 0.0, "bad": 0.0}

for upperKey in features:

print ("\nComparing this blog to " + upperKey.upper() + " blogs:\n")

for lowerKey in features[upperKey]:

if lowerKey == "words":

for word in features[upperKey][lowerKey]:

if word[0] not in terms.stopWords():

if word[0] in blog:

print ("Word found in " + upperKey + " blog: " + word[0])

scores[upperKey] += word[1] * 100 # If a word is found, update the score relative to its TFIDF score.

elif lowerKey == "religion": # This next section is to compare the density of a term of the new blog compared to the density of that term in the analyzed blogs.

scores[upperKey] -= abs(features[upperKey][lowerKey] - analysisOutputs.religionScore)

print ("Religion variance: " + str(features[upperKey][lowerKey] - analysisOutputs.religionScore))

elif lowerKey == "government":

scores[upperKey] -= abs(features[upperKey][lowerKey] - analysisOutputs.governmentScore)

print ("Government variance: " + str(abs(features[upperKey][lowerKey] - analysisOutputs.governmentScore)))

elif lowerKey == "weaponry":

scores[upperKey] -= abs(features[upperKey][lowerKey] - analysisOutputs.weaponryScore)

print ("Weaponry variance: " + str(abs(features[upperKey][lowerKey] - analysisOutputs.weaponryScore)))

elif lowerKey == "names":

scores[upperKey] -= abs(features[upperKey][lowerKey] - analysisOutputs.namesScore)

print ("Names variance: " + str(abs(features[upperKey][lowerKey] - analysisOutputs.namesScore)))

print ("\nFinal Scores:\n" + "Bad: " + str(scores["bad"]) + "\nGood: " + str(scores["good"]) + "\n")

if abs(scores["good"] - scores["bad"]) < .5:

print ("This post does not trend towards 'good; or 'bad'.")

else:

if scores["good"] > scores["bad"]:

print ("This post has been marked as 'good'.")

goodBlogList.append(tb(blog)) # Add term to the blog list. If this program were running constantly, it would be included in the next baes analysis.

else:

print ("This post has been flagged as 'bad'.")

badBlogList.append(tb(blog))

newBlog = None

if blogFile != None:

if blogText != None:

print ("[warn] Both commandline and file blogs were found, prioritizing for use of the file")

newBlog = fileParser.getBlog(blogFile)

elif blogText != None:

# build JSON object via blogTitle, blogAuthor, and blogText

newBlog = BlogObject(blogText, blogAuthor, blogTitle)

else:

print ("[err] Error in creating new blog object...")

return False

# Takes in commandLine args, and sorts variables if necessary.

parser = argparse.ArgumentParser(description='Analyze Blogs.', formatter_class=RawTextHelpFormatter)

parser.add_argument('-b', '--blog', help='Manually enter the blog text here as a string. Formatted like:\n\nauthor: "authors name"\ntitle: "title"\nblog: "blog text"', default=None)

parser.add_argument('-a', '--author', help='Enter the authors name as a string', default=None)

parser.add_argument('-t', '--title', help='Enter the blogs title as a string', default=None)

parser.add_argument('-i', '--inFile', help='Enter the path to a plain text file with the blog entry in it', default=None)

args = parser.parse_args()

# Save variables from commandline args

newBlogFile = args.inFile

newBlogText = args.blog

newBlogAuthor = args.author

newBlogTitle = args.title

go = True

while(go):

# The below object is a dictionary of 2 dictionaries, good and bad features, and their relevant metadata.

# count is the number of times blogs have been passed through. This is necessary for updates.

features = {"good":{"count": 0, "words": [], "names": 0.0, "religion": 0.0, "weaponry": 0.0, "government": 0.0}, "bad": {"count": 0, "words": [], "names": 0.0, "religion": 0.0, "weaponry": 0.0, "government": 0.0}}

json_data = importJSON("Writings/writings.json") # get JSON data, creating a dictionary-like object

# Declaring lists of writings

badBlogList = []

goodBlogList = []

# Analyze the current data in the JSON file.

for blog in json_data["writings"]["bad"]:

badBlogList.append(tb(blog["post"]))

for blog in json_data["writings"]["good"]:

goodBlogList.append(tb(blog["post"]))

analysisResults = analyzeBlogs(badBlogList)

features["bad"]["count"], features["bad"]["words"], features["bad"]["names"], features["bad"]["religion"], features["bad"]["weaponry"], features["bad"]["government"] = len(badBlogList), analysisResults.outputsWordsArray, analysisResults.namesScore, analysisResults.religionScore, analysisResults.weaponryScore, analysisResults.governmentScore

analysisResults = analyzeBlogs(goodBlogList)

features["good"]["count"], features["good"]["words"], features["good"]["names"], features["good"]["religion"], features["good"]["weaponry"], features["good"]["government"] = len(goodBlogList), analysisResults.outputsWordsArray, analysisResults.namesScore, analysisResults.religionScore, analysisResults.weaponryScore, analysisResults.governmentScore

print("Current writings in database have been analyzed... \nRunning comparisons against provided writing...\n ----------------------------")

newBlog = None

# Analyze new file

if newBlogFile is not None:

newBlog = buildNewBlog(newBlogFile)

elif newBlogText is not None:

newBlog = buildNewBlog(None, newBlogAuthor, newBlogTitle, newBlogText)

if newBlog is not None:

tempFeatures = {"words": [], "names": 0.0, "religion": 0.0, "weaponry": 0.0, "government": 0.0}

analyzeNewBlog(newBlog.post, goodBlogList, badBlogList, features)

print ("Please enter another file for analysis. or 'quit' to quit.\n")

newBlogFile = input('File path: ')

if newBlogFile == "quit" or newBlogFile == "Quit" or newBlogFile == "q":

go = False

print("Closing program...")