Ranking code

def flatten_job_query_and_remove_duplicates(job_query):
        modified_job_query = []
        #print "job_query = ",  job_query
        #print "Length of job query is ", len(job_query)
        weighted_keywords_dict = {}
        weight_current = 0.5
        for i in range(0, len(job_query)):
                query_string = job_query[i].decode('utf-8')
                #print "query_string = ", query_string.encode('utf-8')
                for word in query_string.split(" "):
                        if word not in modified_job_query:
                                modified_job_query.append(word)
                                weighted_keywords_dict[word] = weight_current
                weight_current = weight_current / 2
        #print "Flattened job query = ", modified_job_query
        return weighted_keywords_dict

def get_keyword_score(job_query, keywords):
        num_keywords_in_query = 0
        tweet_text_in_lower_case = job_query.lower()
        keywords_lower_case = {}
        for keyword in keywords:
                keywords_lower_case[keyword.lower()] = keywords[keyword]
        for keyword in keywords_lower_case:
                if keyword in tweet_text_in_lower_case:
                        num_keywords_in_query = num_keywords_in_query + keywords_lower_case[keyword]
        return num_keywords_in_query

def compute_rank_of_tweet(tweet, job_query):
        len_tweet = len(tweet["text"])
        tweet_len_score = 0.3 * len_tweet / 140.0
        # print tweet["text"]
        print len_tweet
        keywords = flatten_job_query_and_remove_duplicates(job_query)
        print keywords
        # print tweet["text"]
        keyword_score = 0.7 * get_keyword_score(tweet["text"], keywords)
        print keyword_score
        score = tweet_len_score + keyword_score
        return score