代码

由于这是一个比较复杂的项目，无法一一列出所有的代码。但是，我可以给出一些可能用到的代码片段和思路：

1. 数据收集和预处理

使用Python中的 tweepy 库来连接Twitter API，并使用 nltk 库完成分词和停用词去除等预处理任务。

import tweepy
import nltk
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize

# Twitter API认证
consumer_key = 'your_consumer_key'
consumer_secret = 'your_consumer_secret_key'
access_token = 'your_access_token'
access_token_secret = 'your_access_token_secret'

auth = tweepy.OAuthHandler(consumer_key, consumer_secret)
auth.set_access_token(access_token, access_token_secret)

api = tweepy.API(auth)

# 获取推文
tweets = api.search(q='keyword', count=100)

# 分词和停用词去除
stop_words = set(stopwords.words('english'))

for tweet in tweets:
    text = tweet.text
    words = word_tokenize(text)
    words = [word.lower() for word in words if word.isalpha() and word.lower() not in stop_words]
    # 处理分词后的单词列表
    ...

2. 特征提取

使用Python中的 sklearn 库完成词袋模型和TF-IDF特征提取任务。

from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfVectorizer

# 词袋模型
vectorizer = CountVectorizer()
X_train = vectorizer.fit_transform(train_data)

# TF-IDF特征提取
tfidf_vectorizer = TfidfVectorizer(stop_words='english')
X_train = tfidf_vectorizer.fit_transform(train_data)

3. 模型训练和选择

使用Python中的 sklearn 库完成模型训练和选择任务。

from sklearn.naive_bayes import MultinomialNB
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import cross_val_score

# 训练模型
clf = MultinomialNB()
clf.fit(X_train, y_train)

# 模型选择
params = {'C': [1, 10, 100, 1000], 'kernel': ['linear', 'rbf']}
svm = SVC()
grid_search = GridSearchCV(svm, params)
grid_search.fit(X_train, y_train)

# 交叉验证
scores = cross_val_score(clf, X_train, y_train, cv=5)

4. 模型评估和优化

使用Python中的 sklearn 库完成模型评估和优化任务。

from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score

# 评估模型
y_pred = clf.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
precision = precision_score(y_test, y_pred, average='macro')
recall = recall_score(y_test, y_pred, average='macro')
f1 = f1_score(y_test, y_pred, average='macro')

# 模型优化
clf = MultinomialNB(alpha=0.1)
clf.fit(X_train, y_train)

5. 结果展示

使用Python中的 matplotlib 库完成结果展示任务。

import matplotlib.pyplot as plt

# 生成饼图
labels = ['Positive', 'Negative', 'Neutral']
sizes = [pos_count, neg_count, neu_count]
colors = ['green', 'red', 'gray']
plt.pie(sizes, labels=labels, colors=colors, autopct='%1.1f%%', startangle=90)
plt.axis('equal')
plt.show()

# 生成柱状图
x = ['Accuracy', 'Precision', 'Recall', 'F1-score']
y = [accuracy, precision, recall, f1]
plt.bar(x, y)
plt.show()

以上仅为代码片段，具体实现还需要根据实际情况进行调整和优化。